Ear-worn electronic device employing acoustic environment adaptation

ABSTRACT

An ear-worn electronic device comprises at least one microphone configured to sense sound in an acoustic environment, an acoustic transducer, and a non-volatile memory configured to store a plurality of parameter value sets, each of the parameter value sets associated with a different acoustic environment. A control input is configured to receive a control input signal produced by at least one of a user-actuatable control of the ear-worn electronic device and an external electronic device communicatively coupled to the ear-worn electronic device in response to a user action. A processor is operably coupled to the microphone, the acoustic transducer, the non-volatile memory, and the control input. The processor is configured to classify the acoustic environment using the sensed sound and apply, in response to the control input signal, one of the parameter value sets appropriate for the classification.

TECHNICAL FIELD

This application relates generally to ear-level electronic systems anddevices, including hearing aids, personal amplification devices, andhearables.

BACKGROUND

Hearing devices provide sound for the user. Some examples of hearingdevices are headsets, hearing aids, speakers, cochlear implants, boneconduction devices, and personal listening devices.

SUMMARY

Embodiments are directed to an ear-worn electronic device configured tobe worn in, on or about an ear of a wearer. The device comprises atleast one microphone configured to sense sound in an acousticenvironment and a speaker or a receiver. A non-volatile memory isconfigured to store a plurality of parameter value sets, each of theparameter value sets associated with a different acoustic environment.The device comprises a user-actuatable control. A processor is operablycoupled to the microphone, the speaker or the receiver, the non-volatilememory, and the user-actuatable control. The processor is configured toclassify the acoustic environment using the sensed sound and, inresponse to actuation of the user-actuatable control by the wearer,apply one of the parameter value sets appropriate for theclassification.

Embodiments are directed to an ear-worn electronic device configured tobe worn in, on or about an ear of a wearer. The device comprises atleast one microphone configured to sense sound in an acousticenvironment, an acoustic transducer, and a non-volatile memoryconfigured to store a plurality of parameter value sets, each of theparameter value sets associated with a different acoustic environment. Acontrol input of the device is configured to receive a control inputsignal produced by at least one of a user-actuatable control of theear-worn electronic device and an external electronic devicecommunicatively coupled to the ear-worn electronic device in response toa user action. A processor is operably coupled to the microphone, theacoustic transducer, the non-volatile memory, and the control input. Theprocessor is configured to classify the acoustic environment using thesensed sound and apply, in response to the control input signal, one ofthe parameter value sets appropriate for the classification. Theprocessor can be configured to apply one of the parameter value setsthat enhance intelligibility of speech in the acoustic environment.

Embodiments are directed to an ear-worn electronic device configured tobe worn in, on or about an ear of a wearer. The device comprises atleast one microphone configured to sense sound in an acousticenvironment and a speaker or a receiver. A non-volatile memory isconfigured to store a plurality of parameter value sets, each of theparameter value sets associated with a different acoustic environment.The device comprises a user-actuatable control and at least one activitysensor. A processor is operably coupled to the microphone, the speakeror the receiver, the non-volatile memory, the activity sensor, and theuser-actuatable control. The processor is configured to classify theacoustic environment using the sensed sound and determine an activitystatus of the wearer. The processor is further configured to apply oneof the parameter value sets appropriate for the classification and theactivity status in response to actuation of the user-actuatable controlby the wearer.

Embodiments are directed to an ear-worn electronic device configured tobe worn in, on or about an ear of a wearer. The device comprises atleast one microphone configured to sense sound in an acousticenvironment and a speaker or a receiver. A non-volatile memory isconfigured to store a plurality of parameter value sets, each of theparameter value sets associated with a different acoustic environment.The device comprises a user-actuatable control and a sensor arrangementcomprising one or more sensors configured to sense one or more of aphysical state, a physiologic state, and an activity status of thewearer and to produce sensor signals. A processor is operably coupled tothe microphone, the speaker or the receiver, the non-volatile memory,the sensor arrangement, and the user-actuatable control. The processoris configured to classify the acoustic environment using at least thesensed sound and apply one of the parameter value sets appropriate forthe classification in response to actuation of the user-actuatablecontrol by the wearer and the sensor signals.

Embodiments are directed to a method implemented by an ear-wornelectronic device configured to be worn in, on or about an ear of awearer. The method comprises storing a plurality of parameter value setsin non-volatile memory of the device, each of the parameter value setsassociated with a different acoustic environment. The method comprisessensing sound in an acoustic environment, and classifying, by aprocessor of the device, the acoustic environment using the sensedsound. The method also comprises receiving, from the wearer, a userinput via a user-actuatable control of the device. The method furthercomprises applying, by the processor, one of the parameter value setsappropriate for the classification in response to the user input.

Embodiments are directed to a method implemented by an ear-wornelectronic device configured to be worn in, on or about an ear of awearer. The method comprises storing a plurality of parameter value setsin non-volatile memory of the device, each of the parameter value setsassociated with a different acoustic environment. The method comprisessensing sound in an acoustic environment, and classifying, by aprocessor of the device, the acoustic environment using the sensedsound. The method also comprises receiving, from the wearer, a userinput via a user-actuatable control of the device. The method furthercomprises determining, by the processor, an activity status of thewearer via a sensor arrangement. The method also comprises applying, bythe processor, one of the parameter value sets appropriate for theclassification and the activity status in response to the user input.

Embodiments are directed to a method implemented by an ear-wornelectronic device configured to be worn in, on or about an ear of awearer. The method comprises storing a plurality of parameter value setsin non-volatile memory of the device, each of the parameter value setsassociated with a different acoustic environment. The method comprisessensing sound in an acoustic environment, and classifying, by aprocessor of the device, the acoustic environment using the sensedsound. The method also comprises receiving, from the wearer, a userinput via a user-actuatable control of the device. The method furthercomprises sensing, using a sensor arrangement, one or more of a physicalstate, a physiologic state, and an activity status of the wearer andproducing sensor signals by the sensor arrangement. The method alsocomprises applying, by the processor, one of the parameter value setsappropriate for the classification in response to actuation of theuser-actuatable control by the wearer and the sensor signals.

Embodiments are directed to a method implemented by an ear-wornelectronic device configured to be worn in, on or about an ear of awearer. The method comprises storing a plurality of parameter value setsin non-volatile memory of the device, each of the parameter value setsassociated with a different acoustic environment, sensing sound in anacoustic environment, and classifying, by a processor of the device, theacoustic environment using the sensed sound. The method also comprisesreceiving, by the processor, a control input signal produced by at leastone of a user-actuatable control of the device and an externalelectronic device communicatively coupled to the device in response to auser action. The method further comprises applying, by the processor inresponse to the control input signal, one of the parameter value setsappropriate for the classification. In some embodiments, the method alsocomprises sensing, using a sensor arrangement of the device, one or moreof a physical state, a physiologic state, and an activity status of thewearer, and producing, by the sensor arrangement, sensor signalsindicative of one or more of the physical state, the physiologic state,and the activity status of the wearer. The method further comprisesapplying, by the processor in response to the control input signal, oneof the parameter value sets appropriate for the classification and oneor more of the physical state, the physiologic state, and the activitystatus of the wearer.

Embodiments are directed to an ear-worn electronic device configured tobe worn in, on or about an ear of a wearer. The device comprises atleast one microphone configured to sense sound in an acousticenvironment, an acoustic transducer, and a non-volatile memoryconfigured to store a plurality of parameter value sets each associatedwith a different acoustic environment, wherein at least one or more ofthe parameter value sets are associated with an acoustic environmentwith muffled speech. The device also comprises a control inputconfigured to receive a control input signal produced by at least one ofa user-actuatable control of the ear-worn electronic device, a sensor ofthe ear-worn electronic device, and an external electronic devicecommunicatively coupled to the ear-worn electronic device. The devicefurther comprises a processor operably coupled to the microphone, thespeaker or the receiver, the non-volatile memory, and the control input.The processor is configured to classify the acoustic environment as onewith muffled speech using the sensed sound and, in response to a signalreceived from the control input, apply one or more of the parametervalue sets appropriate for the classification to enhance intelligibilityof muffled speech.

Embodiments are directed to an ear-worn electronic device configured tobe worn in, on or about an ear of a wearer. The device comprises atleast one microphone configured to sense sound in an acousticenvironment, an acoustic transducer, and a non-volatile memoryconfigured to store a plurality of parameter value sets each associatedwith a different acoustic environment, wherein at least one or more ofthe parameter value sets are associated with an acoustic environmentwith muffled speech. The device also comprises a control inputconfigured to receive a control input signal produced by at least one ofa user-actuatable control of the ear-worn electronic device, a sensor ofthe ear-worn electronic device, and an external electronic devicecommunicatively coupled to the ear-worn electronic device. The devicefurther comprises a processor operably coupled to the microphone, theacoustic transducer, the non-volatile memory, and the control input. Theprocessor is configured to classify the acoustic environment as one withmuffled speech using the sensed sound and, in response to a signalreceived from the control input, apply one or more of the parametervalue sets appropriate for the classification to enhance intelligibilityof muffled speech. In some embodiments, the processor is configured toclassify the acoustic environment and detect a change in gain forfrequencies within a specified frequency range relative to a baseline inresponse to receiving the control input signal, wherein the change ingain is indicative of the presence of muffled speech.

Embodiments are directed to a method implemented by an ear-wornelectronic device configured to be worn in, on or about an ear of awearer. The method comprises storing a plurality of parameter value setsin non-volatile memory of the device, each of the parameter value setsassociated with a different acoustic environment, wherein at least oneor more of the parameter value sets are associated with an acousticenvironment with muffled speech delivered by one or more masked personswithin the acoustic environment. The method also comprises sensing soundin an acoustic environment, and classifying, by a processor of thedevice using the sensed sound, the acoustic environment as one withmuffled speech. The method further comprises receiving a signal from acontrol input of the device, and applying, by the processor in responseto the control input signal, one or more of the parameter value setsappropriate for the classification to enhance intelligibility of muffledspeech.

Embodiments are directed to a method implemented by an ear-wornelectronic device configured to be worn in, on or about an ear of awearer. The method comprises storing a plurality of parameter value setsin non-volatile memory of the device, each of the parameter value setsassociated with a different acoustic environment, wherein at least oneor more of the parameter value sets are associated with an acousticenvironment with muffled speech. The method also comprise sensing soundin an acoustic environment, and classifying, by a processor of thedevice using the sensed sound, the acoustic environment as one withmuffled speech. The method further comprises receiving a signal from acontrol input of the device, and applying, by the processor in responseto the control input signal, one or more of the parameter value setsappropriate for the classification to enhance intelligibility of muffledspeech.

The above summary is not intended to describe each disclosed embodimentor every implementation of the present disclosure. The figures and thedetailed description below more particularly exemplify illustrativeembodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

Throughout the specification reference is made to the appended drawingswherein:

FIG. 1A illustrates an ear-worn electronic device in accordance with anyof the embodiments disclosed herein;

FIG. 1B illustrates a system comprising left and right ear-wornelectronic devices of the type shown in FIG. 1A in accordance with anyof the embodiments disclosed herein;

FIG. 1C illustrates an ear-worn electronic device in accordance with anyof the embodiments disclosed herein;

FIG. 1D illustrates an ear-worn electronic device in accordance with anyof the embodiments disclosed herein;

FIG. 2 illustrates a method of implementing an acoustic environmentadaptation feature of an ear-worn electronic device in accordance withany of the embodiments disclosed herein;

FIG. 3 illustrates a method of implementing an acoustic environmentadaptation feature of an ear-worn electronic device in accordance withany of the embodiments disclosed herein;

FIG. 4 illustrates a method of implementing an acoustic environmentadaptation feature of an ear-worn electronic device in accordance withany of the embodiments disclosed herein;

FIG. 5 illustrates a method of implementing an acoustic environmentadaptation feature of an ear-worn electronic device in accordance withany of the embodiments disclosed herein;

FIG. 6 illustrates a method of implementing an acoustic environmentadaptation feature of an ear-worn electronic device in accordance withany of the embodiments disclosed herein;

FIG. 7 illustrates a processor and non-volatile memory of an ear-wornelectronic device configured to implement an acoustic environmentadaptation feature in accordance with any of the embodiments disclosedherein;

FIG. 8 illustrates a method of implementing an acoustic environmentadaptation feature of an ear-worn electronic device in accordance withany of the embodiments disclosed herein;

FIG. 9 illustrates a method of implementing an acoustic environmentadaptation feature of an ear-worn electronic device in accordance withany of the embodiments disclosed herein;

FIG. 10 illustrates various types of parameter value set data that canbe stored in non-volatile memory and operated on by a processor of anear-worn electronic device in accordance with any of the embodimentsdisclosed herein;

FIG. 11 illustrates a method of implementing an acoustic environmentadaptation feature of an ear-worn electronic device in accordance withany of the embodiments disclosed herein;

FIG. 12 illustrates a processor and non-volatile memory of an ear-wornelectronic device configured to implement an acoustic environmentadaptation feature in accordance with any of the embodiments disclosedherein.

FIG. 13 illustrates a method of implementing an acoustic environmentadaptation feature of an ear-worn electronic device in accordance withany of the embodiments disclosed herein;

FIGS. 14A-14C illustrate different displays of a smartphone configuredto facilitate connectivity and interaction with an ear-worn electronicdevice for implementing features of an Edge Mode, a Mask Mode or othermode of the ear-worn electronic device in accordance with any of theembodiments disclosed herein; and

FIG. 15 illustrates a processor, a machine learning processor, and anon-volatile memory of an ear-worn electronic device configured toimplement an acoustic environment adaptation feature in accordance withany of the embodiments disclosed herein.

The figures are not necessarily to scale. Like numbers used in thefigures refer to like components. However, it will be understood thatthe use of a number to refer to a component in a given figure is notintended to limit the component in another figure labeled with the samenumber.

DETAILED DESCRIPTION

Embodiments disclosed herein are directed to any ear-worn or ear-levelelectronic device, including cochlear implants and bone conductiondevices, without departing from the scope of this disclosure. Thedevices depicted in the figures are intended to demonstrate the subjectmatter, but not in a limited, exhaustive, or exclusive sense. Ear-wornelectronic devices (also referred to herein as “hearing devices”), suchas hearables (e.g., wearable earphones, ear monitors, earbuds,electronic earplugs), hearing aids, hearing instruments, and hearingassistance devices, typically include an enclosure, such as a housing orshell, within which internal components are disposed. Typical componentsof a hearing device can include a processor (e.g., a digital signalprocessor or DSP), memory circuitry, power management and chargingcircuitry, one or more communication devices (e.g., one or more radios,a near-field magnetic induction (NFMI) device), one or more antennas,one or more microphones, buttons and/or switches, and areceiver/speaker, for example. Hearing devices can incorporate along-range communication device, such as a Bluetooth® transceiver orother type of radio frequency (RF) transceiver. A communication facility(e.g., a radio or NFMI device) of a hearing device system can beconfigured to facilitate communication between a left hearing device anda right hearing device of the hearing device system.

The term hearing device of the present disclosure refers to a widevariety of ear-level electronic devices that can aid a person withimpaired hearing. The term hearing device also refers to a wide varietyof devices that can produce processed sound for persons with normalhearing. Hearing devices include, but are not limited to, behind-the-ear(BTE), in-the-ear (ITE), in-the-canal (ITC), invisible-in-canal (IIC),receiver-in-canal (RIC), receiver-in-the-ear (RITE) orcompletely-in-the-canal (CIC) type hearing devices or some combinationof the above. Throughout this disclosure, reference is made to a“hearing device,” which is understood to refer to a system comprising asingle left ear device, a single right ear device, or a combination of aleft ear device and a right ear device.

Users of hearing devices (e.g., hearing aid users) are typically exposedto a variety of listening situations, such as speech, speech with noise,speech with music, speech muffled by protective masks (e.g., for virusprotection), music and/or noisy environments. To yield an enhancedlistening experience for hearing device users, the behavior of thedevice, for example the activation of a directional microphone or thecompression/expansion parameters, should adapt to the user's currentacoustic environment. This indicates the need for sound classificationalgorithms functioning as a front end to the rest of the signalprocessing scheme housed in the hearing device.

It has been found that a single set of hearing device parameters is notsufficient to optimally configure a hearing device for all acousticenvironments and listening intents. To address this deficiency, somehearing devices utilize multiple parameter memories, each designed for aspecific acoustic environment. The memory parameters are typically setup during the hearing-aid fitting and are designed for commonproblematic listening situations. During operation, hearing devicewearers typically use a push button to cycle through the memories toaccess the appropriate settings for a given situation. A disadvantage ofthis approach is that wearers have to cycle through their memories, andthey have to remember which memories are best for specific conditions.From a usability perspective, this limits the number of memories andsituations a typical hearing device wearer can effectively employ.

Acoustic environment adaptation has been developed, wherein a mechanismto automatically classify the current acoustic environment drivesautomatic parameter changes to improve operation for that specificenvironment. A disadvantage to this approach is that the automaticchanges are not always desired and can be distracting when the hearingdevice wearer is in a dynamic acoustic environment and the adaptationsoccur frequently. Extended customization via a connected mobile devicehas also been developed, which can be utilized by hearing device wearersto modify and store configurations for future use. Technically, thisapproach has the most flexibility for configuring and optimizing hearingdevice parameters for specific listening situations. However, thismethod depends on the connection to a mobile device and sometimes thisconnection is not available, e.g., if the mobile device is not nearby.This approach can also be unduly challenging to less sophisticatedhearing device wearers.

According to any of the embodiments disclosed herein, a hearing deviceis configured with a mechanism which allows a hearing device wearer tooptimally and automatically set hearing device parameters for theircurrent acoustic environment and listening intent through a simple,single interaction with the hearing device, such as by simply pressing abutton or activating a control on the hearing device and/orautomatically or semi-automatically by the hearing device in response toone or more control input signals generated by one or more sensors ofthe hearing device and/or a communication device communicatively coupledto the hearing device. In some configurations, the hearing device can beconfigured with a mechanism which allows a hearing device wearer tooptimally and automatically set hearing device parameters for theircurrent acoustic environment and listening intent in response to acontrol input signal generated by an external electronic device (e.g., asmartphone or a smart watch) via a user action and received by acommunication device of the hearing device. In accordance with somemechanisms, the wearer of the hearing device volitionally (e.g.,physically) activates a mechanism which allows the wearer to optimallyand automatically set hearing device parameters for their currentacoustic environment and listening intent. In accordance with othermechanisms, the wearer of the hearing device volitionally (e.g.,physically) activates a mechanism feature which, subsequent to useractuation, facilitates optimal and automatic setting of hearing deviceparameters for the wearer's current acoustic environment and listeningintent.

Some of the disclosed mechanisms to assess the acoustic environment anduser activity are contained completely on the hearing device, withoutthe need for connection/communication with a mobile device or internet.Hearing device wearers do not have to remember which program memory isused for which acoustic situation—instead, they simply get the bestsettings for their current situation through the simple press of abutton or control on the hearing device or via a control input signalgenerated by a sensor of the hearing device or received from an externalelectronic device (e.g., a smartphone or a smart watch). Hearing devicewearers are not subject to parameter changes when they don't want them(e.g., there can be no automatic adaptation involved in some modes). Allparameter changes can be user-driven and are optimal for the wearer'scurrent listening situation.

A hearing device according to various embodiments is configured todetect a discrete set of listening situations, through monitoringacoustic characterization variables in the hearing device aid as well as(optionally) activity monitoring data. For these discrete set ofsituations, parameters (e.g., parameter offsets) are created during thefitting process and stored on the hearing device. When the hearingdevice wearer pushes the memory button, the current situation isassessed, interpreted, and used to lookup the appropriate parameter setin the stored configurations. The relevant parameters are loaded andmade available in the current active memory for the user to experience.

Any of the embodiments disclosed herein can incorporate a mechanism fora hearing device wearer to optimally and automatically set hearingdevice parameters for their current acoustic environment and in thepresence of persons (e.g., the wearer of the hearing device, otherpersons in proximity to the wearer). This mechanism of the hearingdevice, which is referred to herein as “Edge Mode” for convenience andnot of limitation, can be activated manually by the hearing devicewearer (e.g., via a user-interface input or a smart device input),semi-automatically (e.g., automatically initiated but activated onlyafter a wearer confirmation input) or automatically (e.g., via a sensorinput).

Any of the embodiments disclosed herein can incorporate a mechanism fora hearing device wearer to optimally and automatically set hearingdevice parameters for their current acoustic environment and in thepresence of persons (e.g., the wearer of the hearing device, otherpersons in proximity to the wearer) speaking through a protective maskworn about the face including the mouth. This mechanism of the hearingdevice, which is referred to herein as “Mask Mode” for convenience andnot of limitation, can be activated manually by the hearing devicewearer (e.g., via a user-interface input or a smart device input),semi-automatically (e.g., automatically initiated but activated onlyafter a wearer confirmation input) or automatically (e.g., via a sensorinput).

In general, any of the device, system, and method embodiments disclosedherein can be configured to implement Edge Mode features, Mask Modefeatures, or both Edge Mode and Mask Mode features. Several of thedevice, system, and method embodiments disclosed herein are described asbeing specifically configured to implement Mask Mode features. In suchembodiments, it is understood that such device, system, and methodembodiments can also be configured to implement Edge Mode features inaddition to Mask Mode features. In various embodiments, the Mask Modeand Edge Mode features are implemented using the same or similarprocesses and hardware, but Mask Mode features are more particularlydirected to enhance intelligibility of muffled speech (e.g., speechuttered by persons wearing a protective mask). Edge Mode and/or MaskMode features of the hearing devices, systems, and methods of thepresent disclosure can be implemented using any of the processes and/orhardware disclosed in commonly-owned U.S. Patent Application Ser. No.62/956,824 filed on Jan. 3, 2020 under Attorney Docket No.ST0891PRV/0532.000891US60, and U.S. Patent Application Ser. No.63/108,765 filed on Nov. 2, 2020 under Attorney Docket No.ST0891PRV2/0532.000891US61, which are incorporated herein by referencein their entireties.

Embodiments of the disclosure are defined in the claims. However, belowthere is provided a non-exhaustive listing of non-limiting Edge Modeexamples. Any one or more of the features of these Edge Mode examplesmay be combined with any one or more features of another example,embodiment, or aspect described herein.

Example Ex1. An ear-worn electronic device configured to be worn in, onor about an ear of a wearer, and comprising at least one microphoneconfigured to sense sound in an acoustic environment, a speaker or areceiver, a non-volatile memory configured to store a plurality ofparameter value sets, each of the parameter value sets associated with adifferent acoustic environment, a user-actuatable control, and aprocessor operably coupled to the microphone, the speaker or thereceiver, the non-volatile memory, and the user-actuatable control, theprocessor configured to classify the acoustic environment using thesensed sound and, in response to actuation of the user-actuatablecontrol by the wearer, apply one of the parameter value sets appropriatefor the classification.

Example Ex2. The device according to Ex1, wherein the processor isconfigured to continuously or repetitively classify the acousticenvironment prior to actuation of the user-actuatable control by thewearer.

Example Ex3. The device according to Ex1 or Ex2, wherein the processoris configured to classify the acoustic environment in response toactuation of the user-actuatable control by the wearer.

Example Ex4. The device according to one or more of Ex1 to Ex3, whereinthe user-actuatable control comprises a button disposed on device.

Example Ex5. The device according to one or more of Ex1 to Ex4, whereinthe user-actuatable control comprises a sensor responsive to a touch ora tap by the wearer.

Example Ex6. The device according to one or more of Ex1 to Ex5, whereinthe user-actuatable control comprises a voice recognition controlimplemented by the processor.

Example Ex7. The device according to one or more of Ex1 to Ex6, whereinthe user-actuatable control comprises gesture detection circuitryresponsive to a wearer gesture made in proximity to the device.

Example Ex8. The device according to one or more of Ex1 to Ex7, whereineach of the parameter value sets comprises a set of gain values or gainoffsets associated with a different acoustic environment.

Example Ex9. The device according to one or more of Ex1 to Ex7, whereineach of the parameter value sets comprises a set of gain values or gainoffsets associated with a different acoustic environment, and a set ofnoise-reduction parameters associated with the different acousticenvironments.

Example Ex10. The device according to one or more of Ex1 to Ex7, whereineach of the parameter value sets comprises a set of gain values or gainoffsets associated with a different acoustic environment, a set ofnoise-reduction parameters associated with the different acousticenvironments, and a set of microphone mode parameters associated withthe different acoustic environments.

Example Ex11. The device according to one or more of Ex1 to Ex7, whereinthe parameter value sets comprises a normal parameter value setassociated with a normal or default acoustic environment, and aplurality of other parameter value sets each associated with a differentacoustic environment.

Example Ex12. The device according to one or more of Ex1 to Ex7, whereinthe parameter value sets comprise a normal parameter value set, and eachof the other parameter value sets define offsets to parameters of thenormal parameter value set.

Example Ex13. The device according to Ex12, wherein the processor iscoupled to a main memory and the normal parameter value set resides inthe main memory, and the processor is configured to select a parametervalue set appropriate for the classification and, in response toactuation of the user-actuatable control by the wearer, apply offsets ofthe selected parameter value set to parameters of the normal parametervalue set residing in the main memory.

Example Ex14. An ear-worn electronic device configured to be worn in, onor about an ear of a wearer, and comprising at least one microphoneconfigured to sense sound in an acoustic environment, a speaker or areceiver, a non-volatile memory configured to store a plurality ofparameter value sets, each of the parameter value sets associated with adifferent acoustic environment, a user-actuatable control, at least oneactivity sensor, and a processor operably coupled to the microphone, thespeaker or the receiver, the non-volatile memory, the activity sensor,and the user-actuatable control, the processor configured to classifythe acoustic environment using the sensed sound and determine anactivity status of the wearer, the processor further configured to applyone of the parameter value sets appropriate for the classification andthe activity status in response to actuation of the user-actuatablecontrol by the wearer.

Example Ex15. The device according to Ex14, wherein the activity sensorcomprises a motion sensor.

Example Ex16. The device according to Ex14 or Ex15, wherein the activitysensor comprises a physiologic sensor.

Example Ex17. The device according to one or more of Ex14 to Ex16,comprising any one or any combination of the components and/or thefunctions of one or more of Ex2 to Ex13.

Example Ex18. An ear-worn electronic device configured to be worn in, onor about an ear of a wearer, and comprising at least one microphoneconfigured to sense sound in an acoustic environment, a speaker or areceiver, a non-volatile memory configured to store a plurality ofparameter value sets, each of the parameter value sets associated with adifferent acoustic environment; a user-actuatable control, a sensorarrangement comprising one or more sensors configured to sense one ormore of a physical state, a physiologic state, and an activity status ofthe wearer and to produce sensor signals, and a processor operablycoupled to the microphone, the speaker or the receiver, the non-volatilememory, the sensor arrangement, and the user-actuatable control, theprocessor configured to classify the acoustic environment using at leastthe sensed sound and apply one of the parameter value sets appropriatefor the classification in response to actuation of the user-actuatablecontrol by the wearer and the sensor signals.

Example Ex19. The device according to Ex18, wherein the processor isconfigured to classify the acoustic environment using the sensed soundand the sensor signals.

Example Ex20. The device according to Ex18 or Ex19, wherein theprocessor is configured to classify the acoustic environment using thesensed sound, and select one of the parameter value sets appropriate forthe classification using the sensor signals.

Example Ex21. The device according to Ex18 or Ex20, wherein theprocessor is configured to classify a sensor output state of one or moreof the sensors using the sensor signals, and apply one of a plurality ofdevice settings stored in the non-volatile memory in response to thesensor output state classification.

Example Ex22. The device according to Ex18 or Ex20, comprising any oneor any combination of the components and/or the functions of one or moreof Ex2 to Ex13.

Example Ex23. A method implemented by an ear-worn electronic deviceconfigured to be worn in, on or about an ear of a wearer, comprisingstoring a plurality of parameter value sets in non-volatile memory ofthe device, each of the parameter value sets associated with a differentacoustic environment, sensing sound in an acoustic environment,classifying, by a processor of the device, the acoustic environmentusing the sensed sound, receiving, from the wearer, a user input via auser-actuatable control of the device, and applying, by the processor,one of the parameter value sets appropriate for the classification inresponse to the user input.

Example Ex24. A method implemented by an ear-worn electronic deviceconfigured to be worn in, on or about an ear of a wearer, comprisingstoring a plurality of parameter value sets in non-volatile memory ofthe device, each of the parameter value sets associated with a differentacoustic environment, sensing sound in an acoustic environment,classifying, by a processor of the device, the acoustic environmentusing the sensed sound, receiving, from the wearer, a user input via auser-actuatable control of the device, determining, by the processor, anactivity status of the wearer via a sensor arrangement, and applying, bythe processor, one of the parameter value sets appropriate for theclassification and the activity status in response to the user input.

Example Ex25. A method implemented by an ear-worn electronic deviceconfigured to be worn in, on or about an ear of a wearer, comprisingstoring a plurality of parameter value sets in non-volatile memory ofthe device, each of the parameter value sets associated with a differentacoustic environment, sensing sound in an acoustic environment,classifying, by a processor of the device, the acoustic environmentusing the sensed sound, receiving, from the wearer, a user input via auser-actuatable control of the device, sensing, using a sensorarrangement, one or more of a physical state, a physiologic state, andan activity status of the wearer and producing sensor signals by thesensor arrangement, and applying, by the processor, one of the parametervalue sets appropriate for the classification in response to actuationof the user-actuatable control by the wearer and the sensor signals.

Example Ex26. The method according to one or more of Ex23 to Ex25,comprising classifying, by the processor, the acoustic environment usingthe sensed sound and the sensor signals.

Example Ex27. The method according to one or more of Ex23 to Ex26,comprising classifying, by the processor, the acoustic environment usingthe sensed sound, and selecting, by the processor, one of the parametervalue sets appropriate for the classification using the sensor signals.

Example Ex28. The method according to one or more of Ex23 to Ex27,comprising classifying, by the processor, a sensor output state of oneor more of the sensors using the sensor signals, and applying, by theprocessor, one of a plurality of device settings stored in thenon-volatile memory in response to the sensor output stateclassification.

Example Ex29. An ear-worn electronic device configured to be worn in, onor about an ear of a wearer, comprising at least one microphoneconfigured to sense sound in an acoustic environment, an acoustictransducer, a non-volatile memory configured to store a plurality ofparameter value sets, each of the parameter value sets associated with adifferent acoustic environment, a control input configured to receive acontrol input signal produced by at least one of a user-actuatablecontrol of the ear-worn electronic device and an external electronicdevice communicatively coupled to the ear-worn electronic device inresponse to a user action, and a processor operably coupled to themicrophone, the acoustic transducer, the non-volatile memory, and thecontrol input, the processor configured to classify the acousticenvironment using the sensed sound and apply, in response to the controlinput signal, one of the parameter value sets appropriate for theclassification.

Example Ex30. The device according to Ex29, wherein the user-actuatablecontrol comprises one or more of a button disposed on the device, asensor responsive to a touch or a tap by the wearer, a voice recognitioncontrol implemented by the processor, and gesture detection circuitryresponsive to a wearer gesture made in proximity to the device, and theexternal electronic device communicatively coupled to the ear-wornelectronic device comprises one or more of a personal digital assistant,a smartphone, a smart watch, a tablet, and a laptop.

Example Ex31. The device according to Ex29 or Ex30, wherein each of theparameter value sets comprises a set of gain values or gain offsetsassociated with a different acoustic environment, and one or both of aset of noise-reduction parameters associated with the different acousticenvironments, and a set of microphone mode parameters associated withthe different acoustic environments.

Example Ex32. The device according to one or more of Ex29 to Ex31,wherein the parameter value sets comprise a normal parameter value setassociated with a normal or default acoustic environment, a plurality ofother parameter value sets each associated with a different acousticenvironment, and each of the other parameter value sets defines offsetsto parameters of the normal parameter value set.

Example Ex33. The device according to one or more of Ex29 to Ex32,comprising a sensor arrangement comprising one or more sensorsconfigured to sense, and produce sensor signals indicative of, one ormore of a physical state, a physiologic state, and an activity status ofthe wearer, and the processor is configured to receive the sensorsignals, classify the acoustic environment using the sensed sound, andapply, in response to the control input, one of the parameter value setsappropriate for the classification and one or more of the physicalstate, the physiologic state, and the activity status of the wearer.

Example Ex34. The device according to Ex33, wherein the one or moresensors comprise one or both of a motion sensor and a physiologicsensor.

Example Ex35. The device according to one or more of Ex29 to Ex34,wherein the processor is configured to apply one of the parameter valuesets that enhance intelligibility of speech in the acoustic environment.

Example Ex36. The device according to one or more of Ex29 to Ex35,wherein the acoustic environment includes muffled speech, and theprocessor is configured to classify the acoustic environment as anacoustic environment including muffled speech using the sensed sound,and apply a parameter value set that enhances intelligibility of muffledspeech.

Example Ex37. The device according to one or more of Ex29 to Ex36,wherein, subsequent to applying an initial parameter value setappropriate for an initial classification of the acoustic environment inresponse to receiving an initial control input signal, the processor isconfigured to automatically apply an adapted parameter value setappropriate for the initial or a subsequent classification of thecurrent acoustic environment in the absence of receiving a subsequentcontrol input signal by the processor.

Example Ex38. The device according to one or more of Ex29 to Ex37,wherein the processor is configured to apply one or more differentparameter value sets appropriate for the classification of the currentacoustic environment in response to one or more subsequently receivedcontrol input signals, learn wearer preferences using utilization dataacquired during application of the different parameter value sets by theprocessor, and adapt selection of subsequent parameter value sets by theprocessor for subsequent use in the current acoustic environment usingthe learned wearer preferences.

Example Ex39. The device according to one or more of Ex29 to Ex38,wherein the processor is configured to apply one or more differentparameter value sets appropriate for the classification of the currentacoustic environment in response to one or more subsequently receivedcontrol input signals, store, in the memory, one or both of utilizationdata and contextual data acquired by the processor during application ofthe different parameter value sets associated with the current acousticenvironment, and adapt selection of subsequent parameter value sets bythe processor for subsequent use in the current acoustic environmentusing one or both of the utilization data and the contextual data.

Example Ex40. The device according to one or more of E37 to Ex39,wherein the processor is configured with instructions to implement amachine learning algorithm to one or more of automatically apply anadapted parameter value set appropriate for the initial or a subsequentclassification of the current acoustic environment, learn wearerpreferences using utilization data acquired during application of thedifferent parameter value sets applied by the processor, adapt selectionof subsequent parameter value sets by the processor for subsequent usein the current acoustic environment using learned wearer preferences,and adapt selection of subsequent parameter value sets for subsequentuse in the current acoustic environment using one or both of utilizationdata and contextual data.

Example Ex41. A method implemented by an ear-worn electronic deviceconfigured to be worn in, on or about an ear of a wearer, comprisingstoring a plurality of parameter value sets in non-volatile memory ofthe device, each of the parameter value sets associated with a differentacoustic environment, sensing sound in an acoustic environment,classifying, by a processor of the device, the acoustic environmentusing the sensed sound, receiving, by the processor, a control inputsignal produced by at least one of a user-actuatable control of thedevice and an external electronic device communicatively coupled to thedevice in response to a user action, and applying, by the processor inresponse to the control input signal, one of the parameter value setsappropriate for the classification.

Example Ex42. The method according to Ex41, comprising sensing, using asensor arrangement of the device, one or more of a physical state, aphysiologic state, and an activity status of the wearer, producing, bythe sensor arrangement, sensor signals indicative of one or more of thephysical state, the physiologic state, and the activity status of thewearer, and applying, by the processor in response to the control inputsignal, one of the parameter value sets appropriate for theclassification and one or more of the physical state, the physiologicstate, and the activity status of the wearer.

Example Ex43. The method according to Ex41 or Ex42, wherein theprocessor is configured with instructions to execute a machine learningalgorithm to implement one or more method steps of one or both of Ex41and Ex42.

FIG. 1A illustrates an ear-worn electronic device 100 in accordance withany of the embodiments disclosed herein. The hearing device 100 includesa housing 102 configured to be worn in, on, or about an ear of a wearer.The hearing device 100 shown in FIG. 1A can represent a single hearingdevice configured for monaural or single-ear operation or one of a pairof hearing devices configured for binaural or dual-ear operation (seee.g., FIG. 1B). The hearing device 100 shown in FIG. 1A includes ahousing 102 within or on which various components are situated orsupported. The housing 102 can be configured for deployment on awearer's ear (e.g., a BTE device housing), within an ear canal of thewearer's ear (e.g., an ITE, ITC, IIC or CIC device housing) or both onand in a wearer's ear (e.g., a RIC or RITE device housing).

The hearing device 100 includes a processor 120 operatively coupled to amain memory 122 and a non-volatile memory 123. The processor 120 isoperatively coupled to components of the hearing device 100 via acommunication bus 121 (e.g., a rigid or flexible PCB). The processor 120can be implemented as one or more of a multi-core processor, a digitalsignal processor (DSP), a microprocessor, a programmable controller, ageneral-purpose computer, a special-purpose computer, a hardwarecontroller, a software controller, a combined hardware and softwaredevice, such as a programmable logic controller, and a programmablelogic device (e.g., FPGA, ASIC). The processor 120 can include or beoperatively coupled to main memory 122, such as RAM (e.g., DRAM, SRAM).The processor 120 can include or be operatively coupled to non-volatilememory 123, such as ROM, EPROM, EEPROM or flash memory. As will bedescribed in detail hereinbelow, the non-volatile memory 123 isconfigured to store a multiplicity of parameter value sets 125, each ofthe parameter value sets associated with a different acousticenvironment.

The hearing device 100 includes an audio processing facility operablycoupled to, or incorporating, the processor 120. The audio processingfacility includes audio signal processing circuitry (e.g., analogfront-end, DSP, and various analog and digital filters), a microphonearrangement 130, and an acoustic transducer 132, such as a speaker or areceiver. The microphone arrangement 130 can include one or morediscrete microphones or a microphone array(s) (e.g., configured formicrophone array beamforming). Each of the microphones of the microphonearrangement 130 can be situated at different locations of the housing102. It is understood that the term microphone used herein can refer toa single microphone or multiple microphones unless specified otherwise.The microphones of the microphone arrangement 130 can be any microphonetype. In some embodiments, the microphones are omnidirectionalmicrophones. In other embodiments, the microphones are directionalmicrophones. In further embodiments, the microphones are a combinationof one or more omnidirectional microphones and one or more directionalmicrophones. One, some, or all of the microphones can be microphoneshaving a cardioid, hypercardioid, supercardioid or lobar pattern, forexample. One, some, or all of the microphones can be multi-directionalmicrophones, such as bidirectional microphones. One, some, or all of themicrophones can have variable directionality, allowing for real-timeselection between omnidirectional and directional patterns (e.g.,selecting between omni, cardioid, and shotgun patterns). In someembodiments, the polar pattern(s) of one or more microphones of themicrophone arrangement 130 can vary depending on the frequency range(e.g., low frequencies remain in an omnidirectional pattern while highfrequencies are in a directional pattern).

Depending on the hearing device implementation, different microphonetechnologies can be used. For example, the hearing device 100 canincorporate any of the following microphone technology types (orcombination of types): MEMS (micro-electromechanical system) microphones(e.g., capacitive, piezoelectric MEMS microphones), moving coil/dynamicmicrophones, condenser microphones, electret microphones, ribbonmicrophones, crystal/ceramic microphones (e.g., piezoelectricmicrophones), boundary microphones, PZM (pressure zone microphone)microphones, and carbon microphones.

The hearing device 100 also includes a user interface comprising auser-actuatable control 127 operatively coupled to the processor 120 viaa control input 129 of the hearing device 100 or the processor 120. Theuser-actuatable control 127 is configured to receive an input from thewearer of the hearing device 100 and, in response, generate a controlinput signal which is communicated to the control input 129. The inputfrom the wearer can be any type of user input, such as a touch input, agesture input, a voice input or a sensor input. The input from thewearer can be a wearer input to an external electronic device 152 (e.g.,a smartphone or a smart watch) communicatively coupled to the hearingdevice 100.

The user-actuatable control 127 can include one or more of a tactileinterface, a gesture interface, and a voice command interface. Thetactile interface can include one or more manually actuatable switches(e.g., a push button, a toggle switch, a capacitive switch). Forexample, the user-actuatable control 127 can include a number ofmanually actuatable buttons or switches disposed on the hearing devicehousing 102. The user-actuatable control 127 can comprises a sensorresponsive to a touch or a tap by the wearer. The user-actuatablecontrol 127 can comprise a voice recognition control implemented by theprocessor 120.

The user-actuatable control 127 can comprise gesture detection circuitryresponsive to a wearer gesture made in proximity to the hearing device100 (e.g., a non-contacting gesture made spaced apart from the device).A single antenna and gesture detection circuitry of the hearing device100 can be used to classify wearer gestures, such as hand or fingermotions made in proximity to the hearing device. As the wearer's hand orfinger moves, the electrical field or magnetic field of the antenna isperturbed. As a result, the antenna input impedance is changed. When awearer performs hand or finger motions (e.g. waving, swipe, tap, holds,zoom, circular movements, etc.), an antenna impedance monitor recordsthe reflection coefficients of the signals or impedance. As the wearer'shand or finger moves, the changes in antenna impedance show uniquepatterns due to the perturbation of the antenna's electrical field ormagnetic field. These unique patterns can correspond to predetermineduser inputs, such as an input to implement an acoustic environmentadaptation feature of the hearing device 100. As will be discussed indetail hereinbelow, the user-actuatable control 127 is configured toreceive an input from the wearer of the hearing device 100 to initiatean acoustic environment adaptation feature of the hearing device 100.

In any of the embodiments disclosed herein, the hearing device 100includes a sensor arrangement 134. The sensor arrangement 134 caninclude one or more sensors configured to sense one or more of aphysical state, a physiologic state, and an activity status of thewearer and to produce sensor signals. The sensor arrangement 134 caninclude a motion sensor arrangement 135. The motion sensor arrangement135 can include one or more sensors configured to sense motion and/or aposition (e.g., physical state and/or activity status) of the wearer ofthe hearing device 100. The motion sensor arrangement 135 can compriseone or more of an inertial measurement unit or IMU, an accelerometer(s),a gyroscope(s), a nine-axis sensor, a magnetometer(s) (e.g., a compass),and a GPS sensor. The IMU can be of a type disclosed in commonly-ownedU.S. Pat. No. 9,848,273, which is incorporated herein by reference. Thesensor arrangement 134 can include physiologic sensor arrangement 137,exclusive of or in addition to the motion sensor arrangement 135. Thephysiologic sensor arrangement 137 can include one or more physiologicsensors including, but not limited to, an EKG or ECG sensor, a pulseoximeter, a respiration sensor, a temperature sensor, a blood pressuresensor, a blood glucose sensor, an EEG sensor, an EMG sensor, an EOGsensor, an electrodermal activity sensor, and a galvanic skin response(GSR) sensor.

The hearing device 100 also includes a classification module 138operably coupled to the processor 120. The classification module 138 canbe implemented in software, hardware, or a combination of hardware andsoftware. The classification module 138 can be a component of, orintegral to, the processor 120 or another processor (e.g., a DSP)coupled to the processor 120. The classification module 138 isconfigured to classify sound in a particular acoustic environment byexecuting a classification algorithm. The processor 120 is configured toprocess sound using an outcome of the classification of the sound forspecified hearing device functions. For example, the processor 120 canbe configured to control different features of the hearing device inresponse to the outcome of the classification by the classificationmodule 138, such as adjusting directional microphones and/or noisereduction settings, for purposes of providing optimum benefit in anygiven listening environment.

The classification module 138 can be configured to detect differenttypes of sound and different types of acoustic environments. Thedifferent types of sound can include speech, music, and severaldifferent types of noise (e.g., wind, transportation noise and vehicles,machinery), etc., and combinations of these and other sounds (e.g.,transportation noise with speech). The different types of acousticenvironments can include a moderately loud restaurant, quiet restaurantspeech, large room speech, sports stadium, concert auditorium, etc.Speech can include clean speech, noisy speech, and muffled speech. Cleanspeech can comprise speech spoken by different peoples at differentreverberation situations, such as a living room or a cafeteria. Noisyspeech can be clean speech mixed randomly with noise (e.g., noise atthree levels of SNR: −6 dB, 0 dB and 6 dB). Machine noise can containnoise generated by various machineries, such as an automobile, a vacuumand a blender. Other sound types or classes can include any sounds thatare not suitably described by other classes, for instance the soundsfrom water running, foot stepping, etc.

According to various embodiments, the classification module 138 can beconfigured to classify sound sensed by the microphone(s) 130 byexecuting a classification algorithm including a Hidden Markov Model(HMM). In some embodiments, the classification module 138 can beconfigured to classify sound sensed by the microphone(s) 130 byexecuting a classification algorithm including a Gaussian model, such asa Gaussian Mixture Model (GMM). In further embodiments, theclassification module 138 can be configured to classify sound sensed bythe microphone(s) 130 by executing other types of classificationalgorithms, such as neural networks, deep neural networks (DNN),regression models, decision trees, random forests, etc.

In various embodiments, the classification module 138 can be configuredto classify sound sensed by the microphone(s) 130 as one of music,speech, and non-speech. The non-speech sound classified by theclassification module 138 can include one of machine noise, wind noise,and other sounds. According to various embodiments, and as disclosed incommonly-owned U.S. Published Patent Application Serial No. 2011/0137656which is incorporated herein by reference, the classification module 138can comprise a feature set having a number of features for soundclassification determined based on performance and computational cost ofthe sound classification. In some implementations, for example, thefeature set can comprise 5 to 7 features, such as Mel-scale Frequencycepstral coefficients (MFCC). In other implementations, the feature setcan comprise low level features.

The hearing device 100 can include one or more communication devices 136coupled to one or more antenna arrangements. For example, the one ormore communication devices 136 can include one or more radios thatconform to an IEEE 802.11 (e.g., WiFi®) or Bluetooth® (e.g., BLE,Bluetooth® 4.2, 5.0, 5.1, 5.2 or later) specification, for example. Itis understood that the hearing device 100 can employ other radios, suchas a 900 MHz radio. In addition, or alternatively, the hearing device100 can include a near-field magnetic induction (NFMI) sensor (e.g., anNFMI transceiver coupled to a magnetic antenna) for effectingshort-range communications (e.g., ear-to-ear communications,ear-to-kiosk communications). Ear-to-ear communications, for example,can be implemented by one or both processors 120 of a pair of hearingdevices 100 when synchronizing the application of a selected parametervalue set 125 during implementation of a user-initiated acousticenvironment adaptation feature in accordance with various embodiments.

The antenna arrangement operatively coupled to the communicationdevice(s) 136 can include any type of antenna suitable for use with aparticular hearing device 100. A representative list of antennasincludes, but are not limited to, patch antennas, planar inverted-Fantennas (PIFAs), inverted-F antennas (IFAs), chip antennas, dipoles,monopoles, dipoles with capacitive-hats, monopoles with capacitive-hats,folded dipoles or monopoles, meandered dipoles or monopoles, loopantennas, Yagi-Udi antennas, log-periodic antennas, spiral antennas, andmagnetic antennas. Many of these types of antenna can be implemented inthe form of a flexible circuit antenna. In such embodiments, the antennais directly integrated into a circuit flex, such that the antenna doesnot need to be soldered to a circuit that includes the communicationdevice(s) 136 and remaining RF components.

The hearing device 100 also includes a power source, which can be aconventional battery, a rechargeable battery (e.g., a lithium-ionbattery), or a power source comprising a supercapacitor. In theembodiment shown in FIG. 1A, the hearing device 100 includes arechargeable power source 124 which is operably coupled to powermanagement circuitry for supplying power to various components of thehearing device 100. The rechargeable power source 124 is coupled tocharging circuity 126. The charging circuitry 126 is electricallycoupled to charging contacts on the housing 102 which are configured toelectrically couple to corresponding charging contacts of a chargingunit when the hearing device 100 is placed in the charging unit.

As was previously discussed, a hearing device system can include a lefthearing device 102 a and a right hearing device 102 b, as is shown inFIG. 1B. The hearing devices 102 a, 102 b are shown to include a subsetof the components shown in FIG. 1A for illustrative purposes. Each ofthe hearing devices 102 a, 102 b includes a processor 120 a, 120 boperatively coupled to non-volatile memory 123 a, 123 b andcommunication devices 136 a, 136 b. In some embodiments, thenon-volatile memory 123 a, 123 b of each hearing device 102 a, 102 b isconfigured to store a plurality of parameter value sets 125 a, 125 beach of which is associated with a different acoustic environment. Inother embodiments, only one of the non-volatile memories 123 a, 123 b isconfigured to store a plurality of parameter value sets 125 a, 125 b. Inaccordance with various embodiments disclosed herein, and afterperforming an acoustic environment classification process, at least oneof the processors 120 a, 120 b is configured to apply one of theparameter value sets 125 a, 125 b stored in at least one of thenon-volatile memories 123 a, 123 b appropriate for the classification.The communication devices 136 a, 136 b are configured to implementear-to-ear communications (e.g., via an RF or NFMI link 140) whensynchronizing the application of a selected parameter value set 125 a,125 b by at least one of the processors 120 a, 120 b duringimplementation of a user-initiated acoustic environment adaptationfeature in accordance with various embodiments.

FIG. 2 illustrates a method of implementing a user-initiated acousticenvironment adaptation feature of an ear-worn electronic device inaccordance with any of the embodiments disclosed herein. The methodshown in FIG. 2 involves storing 202 a plurality of parameter value setin non-volatile memory of the hearing device. Each of the parametervalue sets is associated with a different acoustic environment. Themethod involves sensing 204 sound in acoustic environment using one ormore microphones of the hearing device. The method also involvesclassifying 206, by a processor of the hearing device, the acousticenvironment using the sensed sound. The method further involvesreceiving 208, from the wearer, a user input via a user-actuatablecontrol of the hearing device. The method also involves applying 210, bythe processor, one of the parameter value set appropriate for theclassification in response to the user input.

FIG. 3 illustrates a method of implementing a user-initiated acousticenvironment adaptation feature of an ear-worn electronic device inaccordance with any of the embodiments disclosed herein. The methodshown in FIG. 3 involves storing 302 a plurality of parameter value setsin non-volatile memory of the hearing device. Each of the parametervalue sets is associated with a different acoustic environment. Themethod involves sensing 304 sound in an acoustic environment using oneor more microphones of the hearing device. The method also involvesclassifying 306, by a processor of the hearing device, the acousticenvironment using the sensed sound. The method further involvesreceiving 308, from the wearer, a user input via a user-actuatablecontrol of the hearing device. The method involves determining 310, bythe processor, an activity status of the wearer. The method alsoinvolves applying 312, by the processor, one of the parameter value setappropriate for the classification and the activity status in responseto the user input.

FIG. 4 illustrates a method of implementing a user-initiated acousticenvironment adaptation feature of an ear-worn electronic device inaccordance with any of the embodiments disclosed herein. The methodshown in FIG. 4 involves storing 402 a plurality of parameter value setsin non-volatile memory of the hearing device. Each of the parametervalue sets is associated with a different acoustic environment. Themethod involves sensing 404 sound in an acoustic environment using oneor more microphones of the hearing device. The method also involvesclassifying 406, by a processor of the hearing device, the acousticenvironment using the sensed sound. The method further involvesreceiving 408, from the wearer, a user input via a user-actuatablecontrol of the hearing device. The method involves sensing 410, using asensor arrangement, one or more of a physical state, a physiologicstate, and an activity state of the wearer and producing signals by thesensor arrangement. The method also involves applying 412, by theprocessor, one of the parameter value set appropriate for theclassification in response to the user input and the sensor signals.

By way of example, the wearer may be sitting alone in a moderately loudcafé and engaged in reading a newspaper. According to the methodillustrated in FIG. 4, the processor of the wearer's hearing devicewould classify the acoustic environment generally as a moderately loudrestaurant. In addition, the processor would receive sensor signals froma sensor arrangement of the hearing device which provide an indicationof the wearer's physical state, the physiologic state, and/or activitystatus while present in the current acoustic environment. In thisillustrative example, a motion sensor could sense relatively little orminimal head or neck movement indicative of reading rather than speakingwith a tablemate at the café. The processor could also sense the absenceof speaking by the wearer and/or a nearby person in response to signalsproduced by the microphone(s) of the hearing device. The additionalinformation provided by the sensor arrangement of the hearing deviceprovides contextual or listening intent information which can be used bythe processor to refine the acoustic environment classification. Forexample, without the additional sensor information, the processor wouldconfigure the hearing device for operation in an acoustic environmentclassified as “quiet restaurant speech.” This classification wouldassume that the wearer is engaged in conversation with another personwithin a quiet restaurant environment, which would not be accurate. Inresponse to determining that the wearer is not engaged in conversationbased on sensor signals received from the sensor arrangement, theprocessor of the hearing device would refine the acoustic environmentclassification as “quiet restaurant non-speech” or “quiet restaurantreading,” which would be reflective of the listener's intent within thecurrent acoustic environment.

FIG. 5 illustrates a method of implementing a user-initiated acousticenvironment adaptation feature of an ear-worn electronic device inaccordance with any of the embodiments disclosed herein. The methodshown in FIG. 5 involves storing 502 parameter value sets including aNormal Parameter Value Set and other parameter value sets innon-volatile memory (NVM) of an ear-worn electronic device. Each of theother parameter value sets is associated with a different acousticenvironment and defines offsets to parameters of the Normal ParameterValue Set. The method involves moving/storing the Normal Parameter ValueSet from/in NVM to main memory of the device. The method also involvessensing 506 sound in an acoustic environment using one or moremicrophones of the device. The method further involves classifying 508,by a processor of the device, the acoustic environment using the sensedsound. The method also involves receiving 510, from the wearer, a userinput via a user-actuatable control of the device. The method furtherinvolves applying 512 offsets of the selected parameter value set toparameters of the Normal Parameter Value Set residing in main memory.

FIG. 6 illustrates a process of implementing a user-initiated acousticenvironment adaptation feature of an ear-worn electronic device inaccordance with any of the embodiments disclosed herein. According tothe process shown in FIG. 6, the acoustic environment adaptation featureis initiated in response to a user actuating 600 a control of a hearingdevice. Prior to or after user actuation of the control, an acousticsnapshot of the listening environment is read or interpreted 602 by thehearing device. In some implementations, the hearing device can beconfigured to continuously or repetitively (e.g., every 5, 10, or 30seconds) sense and classify the acoustic environment prior to actuationof the user-actuatable control. In other implementations, the hearingdevice can be configured to classify the acoustic environment inresponse to actuation of the user-actuatable control by the wearer(e.g., after actuation of the user-actuated control). An acousticsnapshot is generated by the hearing device based on the classificationof the acoustic environment. After reading or interpreting 602 theacoustic snapshot, the method involves looking up 604 parameter valuechanges (e.g., offsets) stored in non-volatile memory of the hearingdevice. The method also involves applying 606 parameter value changes tothe hearing device.

The processes shown in FIG. 6 can be initiated and repeated on an“on-demand” basis by the wearer by actuating the user-actuatable controlof the hearing device. This on-demand capability allows the wearer toquickly (e.g., instantly or immediately) configure the hearing devicefor optimal performance in the wearer's current acoustic environment andin accordance with the wearer's listening intent. In contrast,conventional fully-autonomous sound classification techniquesimplemented in hearing devices provide for slow and gradual adaptationto the wearer's current acoustic environment. Moreover, conventionalfully-autonomous sound classification techniques do not always providedesirable sound and can be distracting when the wearer is in a dynamicacoustic environment and the adaptations occur frequently.

FIG. 7 illustrates a processor and non-volatile memory of an ear-wornelectronic device configured to implement a user-initiated acousticenvironment adaptation feature in accordance with any of the embodimentsdisclosed herein. FIG. 7 illustrates additional details of the processesof the method shown in FIG. 4. The processor 710 is operably coupled tonon-volatile memory 702 which is configured to store a number of lookuptables 704, 706.

Lookup table 704 includes a table comprising a plurality of differentacoustic environment classifications 704 a (AEC₁-AEC_(N)). Anon-exhaustive, non-limiting list of different acoustic environmentclassifications 704 a can include, for example, any one or anycombination of speech in quiet, speech in babble noise, speech in carnoise, speech in noise, car noise, wind noise, and other noise. Each ofthe acoustic environment classifications 704 a has associated with it aset of parameter values 704 b (PV₁-PV_(N)) and a set of device settings704 c (DS₁-DS_(N)). The parameter value sets 704 b (PV₁-PV_(N)) caninclude, for example, a set of gain values or gain offsets associatedwith each of the different acoustic environment classifications 704 a(AEC₁-AEC_(N)). The device settings 704 c (DS₁-DS_(N)) can include, forexample, a set of noise-reduction parameters associated with each of thedifferent acoustic environment classifications 704 a (AEC₁-AEC_(N)). Thedevice settings 704 c (DS₁-DS_(N)) can also include, for example, a setof microphone mode parameters (e.g., omni mode, directional mode)associated with each of the different acoustic environmentclassifications 704 a (AEC₁-AEC_(N)).

Lookup table 706 includes a lookup table associated with each of anumber of different sensors of the hearing device. In the illustrativeexample shown in FIG. 7, the lookup table 706 includes table 706-1associated with Sensor A (e.g., an IMU). Sensor A is characterized tohave a plurality of different sensor output states (SOS) 706-1 a(SOS₁-SOS_(N)) of interest. Each of the sensor output states 706-1 a hasassociated with it a set of parameter values 706-1 b (PV₁-PV_(N)) and aset of device settings 706-1 c (DS₁-DS_(N)). The lookup table 706 alsoincludes table 706-N associated with Sensor N (e.g., a physiologicsensor). Sensor N is characterized to have a plurality of differentsensor output states 706-Na (SOS₁-SOS_(N)) of interest (e.g., an IMU canhave sensor output states of sitting, standing, lying down, running,walking, etc.). Each of the sensor output states 706-Na has associatedwith it a set of parameter values 706-Nb (PV₁-PV_(N)) and a set ofdevice settings 706-Nc (DS₁-DS_(N)).

The parameter value sets 706-1 b, 706-Nb (PV₁-PV_(N)) can include, forexample, a set of gain values or gain offsets associated with each ofthe different sensor output states 706-1 a (SOS₁-SOS_(N)). The devicesettings 706-1 c, 706-Nc (DS₁-DS_(N)) can include, for example, a set ofnoise-reduction parameters associated with each of the different sensoroutput states 706-Na (SOS₁-SOS_(N)). The device settings 706-1 c, 706-Nc(DS₁-DS_(N)) can also include, for example, a set of microphone modeparameters (e.g., omni mode, directional mode) associated with each ofthe different sensor output states 706-1 a, 706-Na.

The processor 710 of the hearing device, in response to sensing sound inan acoustic environment using one or more microphones, is configured toclassify the acoustic environment using the sensed sound. Havingclassified the sensed sound, the processor 710 performs a lookup intable 704 to obtain the parameter value set 704 b and device settings704 c that correspond to the acoustic environment classification 704 a.Additionally, the processor 710 performs a lookup in table 706 inresponse to receiving sensor signals from one or more sensors of thehearing device. Having received sensor signals indicative of an outputstate of one or more hearing device sensors, the processor 710 obtainsthe parameter value set 706-1 b, 706-Nb and device settings 706-1 c,706-Nc that correspond to the sensor output state 706-1 a, 706-Na.

After performing lookups in tables 704 and 706, the processor 710 isconfigured to select 712 parameter value sets and device settingsappropriate for the acoustic environment and the received sensorinformation. The main memory (e.g., custom or active memory) of thehearing device is updated 714 in a manner previously described using theselected parameter value sets and device settings. Subsequently, theprocessor 710 processes sound using the parameter value settings anddevice setting residing in the main memory.

Although reference is made herein to the accompanying set of drawingsthat form part of this disclosure, one of at least ordinary skill in theart will appreciate that various adaptations and modifications of theembodiments described herein are within, or do not depart from, thescope of this disclosure. For example, aspects of the embodimentsdescribed herein may be combined in a variety of ways with each other.Therefore, it is to be understood that, within the scope of the appendedclaims, the claimed invention may be practiced other than as explicitlydescribed herein.

According to various embodiments, and with reference to FIG. 1C, a MaskMode mechanism of a hearing device can be activated manually in responseto one or more control input signals generated by a user-actuatablecontrol of the hearing device and/or automatically or semi-automaticallyby the hearing device in response to one or more control input signalsgenerated by one or more sensors. The one or more sensors can beintegral, or separate but communicatively coupled to, the hearingdevice. For example, a body-worn camera and/or a hand-carried camera candetect presence of a mask on the wearer and other persons within theacoustic environment. The camera(s) can communicate a control inputsignal to the hearing device which, in response to the control inputsignal(s), activates a hearing device mechanism (e.g., Mask Modefeature(s)) to optimally and automatically set hearing device parametersappropriate for the current acoustic environment and muffled speechwithin the current acoustic environment to enhance intelligibility ofspeech heard by the hearing device wearer.

According to various embodiments, and with reference to FIG. 1D, a MaskMode mechanism of a hearing device can be activated manually in responseto one or more control input signals generated by a user-actuatablecontrol of the hearing device and/or automatically or semi-automaticallyby the hearing device in response to one or more control input signalsgenerated by one or more sensors and/or a communication devicecommunicatively coupled to the hearing device. The one or more sensorscan be integral, or separate but communicatively coupled to, the hearingdevice, and be of a type described herein (e.g., a camera). Thecommunication device can be any wireless device or system (see examplesdisclosed herein) configured to communicatively to the hearing device.In response to the control input signal(s), a hearing device mechanism(e.g., Mask Mode feature(s)) is activated to optimally and automaticallyset hearing device parameters appropriate for the current acousticenvironment and muffled speech within the current acoustic environmentto enhance intelligibility of speech heard by the hearing device wearer.

By way of example, a hearing device can be configured to automatically(e.g., autonomously) or semi-automatically (e.g., via a control inputsignal received from a smartphone or a smart watch in response to a userinput to the smartphone or smart watch) detect the presence of a maskcovering the face/mouth of a hearing device wearer and, in response,automatically (or semi-automatically via a confirmation input by thewearer via a user-actuatable control or the hearing device or via asmartphone or smart watch) activate a Mask Mode configured to enhanceintelligibility of the wearer's and/or other person's muffled speech.For example, the hearing device can sense for a reduction in gain for aspecified frequency range or a specified frequency band or bands whilemonitoring the wearer's and/or other person's speech in the acousticenvironment. This gain reduction for the specified frequency range/bandis indicative of muffled speech due to the presence of a mask coveringthe wearer's mouth. One or more gain/frequency profiles indicative ofmuffled speech due to the wearing of a mask (e.g., a single mask ordifferent masks) can be developed specifically for the hearing devicewearer or for a population of hearing device wearers. Thepre-established gain/frequency profile(s) can be stored in a memory ofthe hearing device and compared against real-time gain/frequency dataproduced by a processor of the hearing device while monitoring thewearer's and/or other person's speech in the acoustic environment.

In various embodiments, the mechanisms (e.g., Edge Mode and/or MaskMode) to assess the acoustic environment including the presence ofspeaker (which may or may not include masked speakers) within theacoustic environment (and optionally user activity) can be containedcompletely on the hearing device, without the need forconnection/communication with a mobile processing device or theInternet. Hearing device wearers do not have to remember which programmemory is used for which acoustic situation—instead, they simply get thebest settings for their current situation through the simple press of abutton or control on the hearing device or by way of automatic orsemi-automatic activation via a camera and/or other sensor and/or anexternal electronic device (e.g., a smartphone or smart watch). Hearingdevice wearers are not subject to parameter changes if they don't wantthem (e.g., there need not be fully automatic adaptation involved). Allparameter changes can be user-driven and are optimal for the wearer'scurrent listening situation, such as those involving muffled speechdelivered by masked persons within the current acoustic environment.

A hearing device according to various embodiments is configured todetect a discrete set of listening situations, through monitoringacoustic characterization variables in the hearing device as well as(optionally) activity monitoring data. For these discrete set ofsituations, parameters (e.g., parameter offsets) are created during thefitting process and stored on the hearing device. In the case of one ormore Mask Modes of the hearing device, the hearing device can beconfigured to detect a discrete set of listening situations involvingmasked speakers, through monitoring acoustic characterization variablesin the hearing device aid as well as (optionally) activity monitoringdata. For these discrete set of situations, parameters (e.g., parameteroffsets) are created during the fitting process and stored on thehearing device for each of the one or more Mask Modes. When the hearingdevice wearer generates a control input signal via, e.g., pushing amemory button on the hearing device or an activation button presented ona smartphone or smart watch display (with the smartphone or smart watchrunning a hearing device interactive app), for example, the currentacoustic/activity (optional) situation is assessed, interpreted, andused to lookup the appropriate parameter set in the storedconfigurations. The relevant parameters are loaded and made available inthe current active memory for the user to experience.

Mask Mode embodiments of the disclosure are directed to improvingintelligibility of muffled speech communicated to the ear drum of ahearing device wearer when the wearer is within an acoustic environmentin which the hearing device wearer and other persons are speakingthrough a protective mask. Mask Mode embodiments are agnostic withrespect to social distancing and simply optimize speech for enhancedintelligibility. Unlike an approach that merely applies a slight changeof gain in high frequencies, Mask Mode embodiments of the disclosureanalyze the actual voice (acoustic slice) at that time (e.g., inreal-time), in that environment, with the mask in place, and thenselects settings (e.g., individual settings or selected settings from anumber of different presets or libraries of features) that include themost appropriate set of acoustic parameters (compression, gain, etc.)for that specific environment (e.g., with that specific mask, distance,presence of noise, soft speech or loud speech, music, etc.). Asdiscussed previously, Edge Mode embodiments of the disclosure can beimplemented in the same or similar manner as Mask Mode embodiments.

Embodiments of the disclosure are defined in the claims. However, belowthere is provided a non-exhaustive listing of non-limiting Mask Modeexamples. Any one or more of the features of these examples may becombined with any one or more features of another example, embodiment,or aspect described herein.

Example Ex0. An ear-worn electronic device configured to be worn in, onor about an ear of a wearer, comprises at least one microphoneconfigured to sense sound in an acoustic environment, a speaker or areceiver, and a non-volatile memory configured to store a plurality ofparameter value sets each associated with a different acousticenvironment, wherein one or more of the parameter value sets areassociated with an acoustic environment with muffled speech delivered byone or more masked persons within the acoustic environment. A controlinput is operatively coupled to one or both of a user-actuatable controland a sensor-actuatable control, and a processor, operably coupled tothe microphone, the speaker or the receiver, the non-volatile memory,and the control input, is configured to classify the acousticenvironment as one with muffled speech using the sensed sound and, inresponse to a signal received from the control input, apply one or moreof the parameter value sets appropriate for the classification toenhance intelligibility of muffled speech.

Example Ex1. An ear-worn electronic device configured to be worn in, onor about an ear of a wearer, comprises at least one microphoneconfigured to sense sound in an acoustic environment, an acoustictransducer (e.g., a speaker, a receiver, a bone conduction transducer),and a non-volatile memory configured to store a plurality of parametervalue sets each associated with a different acoustic environment,wherein one or more of the parameter value sets are associated with anacoustic environment with muffled speech. A control input is configuredto receive a control input signal produced by at least one of auser-actuatable control of the ear-worn electronic device, a sensor ofthe ear-worn electronic device, and an external electronic devicecommunicatively coupled to the ear-worn electronic device, and aprocessor, operably coupled to the microphone, the acoustic transducer,the non-volatile memory, and the control input, is configured toclassify the acoustic environment as one with muffled speech using thesensed sound and, in response to a signal received from the controlinput, apply one or more of the parameter value sets appropriate for theclassification to enhance intelligibility of muffled speech.

Example Ex2. The device according to Ex0 or Ex1, wherein the processoris configured to apply a first parameter value set to enhanceintelligibility of muffled speech uttered by the wearer of the ear-wornelectronic device, and apply a second parameter value set, differentfrom the first parameter value set, to enhance intelligibility ofmuffled speech uttered by one or more persons other than the wearer ofthe ear-worn electronic device.

Example Ex3. The device according to Ex0 or Ex1, wherein the processoris configured to continuously or repetitively classify the acousticenvironment to monitor for a change in gain for frequencies within aspecified frequency range relative to a baseline prior to receiving thecontrol input signal, and the change in gain is indicative of thepresence of muffled speech.

Example Ex4. The device according to Ex0 or Ex1, wherein the processoris configured to classify the acoustic environment and detect a changein gain for frequencies within a specified frequency range relative to abaseline in response to receiving the control input signal, and thechange in gain is indicative of the presence of muffled speech.

Example Ex5. The device according to Ex3 or Ex4, wherein the baselinecomprises a generic baseline associated with a population ofmask-wearing persons not known by the wearer.

Example Ex6. The device according to Ex3 of Ex4, wherein the baselinecomprises a baseline associated with one or more specified groups ofmask-wearing persons known to the wearer.

Example Ex7. The device according to Ex0 or Ex1, wherein the parametervalue sets associated with an acoustic environment with muffled speechcomprise a plurality of parameter value sets each associated with adifferent type of mask wearable by the one or more masked persons.

Example Ex8. The device according to Ex0 or Ex1, wherein each of theparameter value sets comprises a set of gain values or gain offsetsassociated with a different acoustic environment, and the processor isconfigured to increase the set of gain values or gain offsets for aspecified frequency range in response to classifying the acousticenvironment as one with muffled speech.

Example Ex9. The device according to one or more of Ex2, Ex3, and Ex8,wherein the specific frequency range comprises a frequency range ofabout 0.5 kHz to about 4 kHz.

Example Ex10. The device according to one or more of Ex0 to Ex9, whereineach of the parameter value sets comprises a set of gain values or gainoffsets associated with a different acoustic environment and a set ofnoise-reduction parameters associated with the different acousticenvironments.

Example Ex11. The device according to one or more of Ex0 to Ex9, whereineach of the parameter value sets comprises a set of gain values or gainoffsets associated with a different acoustic environment, a set ofnoise-reduction parameters associated with the different acousticenvironments, and a set of microphone mode parameters associated withthe different acoustic environments.

Example Ex12. The device according to one or more of Ex0 to Ex11,wherein the parameter value sets comprise a normal parameter value setassociated with a normal or default acoustic environment, and aplurality of other parameter value sets each associated with a differentacoustic environment including one or more parameter value setsassociated with an acoustic environment with muffled speech.

Example Ex13. The device according to Ex12, wherein each of the otherparameter value sets define offsets to parameters of the normalparameter value set.

Example Ex14. The device according to Ex13, wherein the processor iscoupled to a main memory and the normal parameter value set resides inthe main memory, and the processor is configured to select a parametervalue set appropriate for the classification and, in response to thecontrol input signal, apply offsets of the selected parameter value setto parameters of the normal parameter value set residing in the mainmemory.

Example Ex15. The device according to one or more of Ex0 to Ex14,wherein the user-actuatable control comprises a button disposed on thedevice.

Example Ex16. The device according to one or more of Ex0 to Ex15,wherein the user-actuatable control comprises a sensor responsive to atouch or a tap by the wearer.

Example Ex17. The device according to one or more of Ex0 to Ex16,wherein the user-actuatable control comprises a voice recognitioncontrol implemented by the processor.

Example Ex18. The device according to one or more of Ex0 to Ex17,wherein the user-actuatable control comprises gesture detectioncircuitry responsive to a wearer gesture made in proximity to thedevice.

Example Ex19. The device according to one or more of Ex0 to Ex18,wherein the sensor-actuatable control comprises a camera carried orsupported by the wearer, and the camera, the processor, or a remoteprocessor communicatively coupled to the device is configured to detectpresence of a mask on the one or more mask-wearing persons within theacoustic environment.

Example Ex20. The device according to Ex19, wherein the camera, theprocessor, or the remote processor is configured to detect the type ofthe mask on the one or more mask-wearing persons.

Example Ex21. The device according to Ex19 or Ex20, wherein the cameracomprises a body-wearable camera.

Example Ex22. The device according to Ex19 or Ex21, wherein the cameracomprises a smartphone camera or a smart watch camera.

Example Ex23. The device according to one or more of Ex1 to Ex22,wherein the external electronic device comprises one or more of apersonal digital assistant, a smartphone, a smart watch, a tablet, and alaptop.

Example Ex24. The device according to one or more of Ex0 to Ex23,wherein the processor is configured to apply one or more differentparameter value sets appropriate for the classification of the currentacoustic environment in response to one or more subsequently receivedcontrol input signals, learn wearer preferences using utilization dataacquired during application of the different parameter value setsapplied by the processor, and adapt selection of subsequent parametervalue sets by the processor for subsequent use in the current acousticenvironment using the learned wearer preferences.

Example Ex25. The device according to one or more of Ex0 to Ex24,wherein the processor is configured to apply one or more differentparameter value sets appropriate for the classification of the currentacoustic environment in response to one or more subsequently receivedcontrol input signals, store, in the memory, one or both of utilizationdata and contextual data acquired by the processor during application ofthe different parameter value sets associated with the current acousticenvironment, and adapt selection of subsequent parameter value sets bythe processor for subsequent use in the current acoustic environmentusing one or both of the utilization data and the contextual data.

Example Ex26. The device according to one or more of Ex0 to Ex25,wherein the processor is configured with instructions to implement amachine learning algorithm to one or more of automatically apply anadapted parameter value set appropriate for an initial or a subsequentclassification of the current acoustic environment, learn wearerpreferences using utilization data acquired during application of thedifferent parameter value sets applied by the processor, adapt selectionof subsequent parameter value sets for subsequent use in the currentacoustic environment using learned wearer preferences, and adaptselection of subsequent parameter value sets for subsequent use in thecurrent acoustic environment using one or both of utilization data andcontextual data.

Example Ex27. A method implemented by an ear-worn electronic deviceconfigured to be worn in, on or about an ear of a wearer comprisesstoring a plurality of parameter value sets in non-volatile memory ofthe device. Each of the parameter value sets is associated with adifferent acoustic environment, wherein one or more of the parametervalue sets are associated with an acoustic environment with muffledspeech. The method comprises sensing sound in an acoustic environment,classifying, by a processor of the device using the sensed sound, theacoustic environment as one with muffled speech, receiving a signal froma control input of the device, and applying, by the processor inresponse to the control input signal, one or more of the parameter valuesets appropriate for the classification to enhance intelligibility ofmuffled speech.

Example Ex28. The method according to Ex27, wherein applying comprisesapplying a first parameter value set to enhance intelligibility ofmuffled speech uttered by the wearer of the ear-worn electronic device,and applying a second parameter value set, different from the firstparameter value set, to enhance intelligibility of muffled speechuttered by one or more persons other than the wearer of the ear-wornelectronic device.

Example Ex29. The method according to Ex27, wherein classifyingcomprises continuously or repetitively classify the acoustic environmentto monitor for a change in gain for frequencies within a specifiedfrequency range relative to a baseline prior to receiving the controlinput signal, and the change in gain is indicative of the presence ofmuffled speech.

Example Ex30. The method according to Ex27, wherein classifyingcomprises classifying the acoustic environment and detecting a change ingain for frequencies within a specified frequency range relative to abaseline in response to receiving the control input signal, and thechange in gain is indicative of the presence of muffled speech.

Example Ex31. The method according to Ex25 or Ex30, wherein the baselinecomprises a generic baseline associated with a population ofmask-wearing persons not known by the wearer.

Example Ex32. The method according to Ex25 or Ex30, wherein the baselinecomprises a baseline associated with one or more specified groups ofmask-wearing persons known to the wearer.

Example Ex33. The method according to Ex27, wherein the parameter valuesets associated with an acoustic environment with muffled speechcomprise a plurality of parameter value sets each associated with adifferent type of mask wearable by the one or more masked persons.

Example Ex34. The method according to Ex27, wherein each of theparameter value sets comprises a set of gain values or gain offsetsassociated with a different acoustic environment, and the processorincreases the set of gain values or gain offsets for a specifiedfrequency range in response to classifying the acoustic environment asone with muffled speech.

Example Ex35. The method according to one or more of Ex29, Ex30, andEx34, wherein the specific frequency range comprises a frequency rangeof about 0.5 kHz to about 4 kHz.

Example Ex36. The method according to one or more of Ex27 to Ex35,wherein each of the parameter value sets comprises a set of gain valuesor gain offsets associated with a different acoustic environment, and aset of noise-reduction parameters associated with the different acousticenvironments.

Example Ex37. The method according to one or more of Ex27 to Ex35,wherein each of the parameter value sets comprises a set of gain valuesor gain offsets associated with a different acoustic environment, a setof noise-reduction parameters associated with the different acousticenvironments, and a set of microphone mode parameters associated withthe different acoustic environments.

Example Ex38. The method according to one or more of Ex27 to Ex37,wherein the parameter value sets comprise a normal parameter value setassociated with a normal or default acoustic environment, and aplurality of other parameter value sets each associated with a differentacoustic environment including one or more parameter value setsassociated with an acoustic environment with muffled speech.

Example Ex39. The method according to Ex38, wherein each of the otherparameter value sets define offsets to parameters of the normalparameter value set.

Example Ex40. The method according to Ex39, wherein the processor iscoupled to a main memory and the normal parameter value set resides inthe main memory, and the processor selects a parameter value setappropriate for the classification and, in response to the control inputsignal, applies offsets of the selected parameter value set toparameters of the normal parameter value set residing in the mainmemory.

Example Ex41. The method according to one or more of Ex27 to Ex40,wherein the control input signal is generated by one or both of auser-actuatable control and a sensor-actuatable control.

Example Ex42. The method according to Ex41, wherein the user-actuatablecontrol comprises a button disposed on the device.

Example Ex43. The method according to Ex41 or Ex42, wherein theuser-actuatable control comprises a sensor responsive to a touch or atap by the wearer.

Example Ex44. The method according to one or more of Ex41 to Ex43,wherein the user-actuatable control comprises a voice recognitioncontrol implemented by the processor.

Example Ex45. The method according to one or more of Ex41 to Ex44,wherein the user-actuatable control comprises gesture detectioncircuitry responsive to a wearer gesture made in proximity to thedevice.

Example Ex46. The method according to one or more of Ex41 to Ex45,wherein the sensor-actuatable control comprises a camera carried orsupported by the wearer, and the camera, the processor, or a remoteprocessor communicatively coupled to the device is configured to detectpresence of a mask on the one or more mask-wearing persons within theacoustic environment.

Example Ex47. The method according to Ex46, wherein the camera, theprocessor, or the remote processor is configured to detect the type ofthe mask on the one or more mask-wearing persons.

Example Ex48. The method according to Ex46 or claim 47, wherein thecamera comprises a body-wearable camera or a camera supported by glassesworn by the wearer.

Example Ex49. The method according to one or more of Ex46 to Ex48,wherein the camera comprises a smartphone camera or a smart watchcamera.

Example Ex50. The device according to one or more of Ex0 to Ex49 whereinthe processor is configured to automatically generate a currentparameter value set in response to a first control input, the currentparameter value set providing a pleasing or preferred listeningexperience for the wearer, the processor also configured to store, inthe non-volatile memory, the current parameter value set as auser-defined memory in the non-volatile memory.

Example Ex51. The device according to Ex50, wherein the processor isconfigured to retrieving the user-defined memory from the non-volatilememory in response to a second control input, and apply the parametervalue set corresponding to the user-defined memory to recreate thepleasing or preferred listening experience for the wearer.

Example Ex52. The method according to one or more of Ex27 to Ex49,comprising automatically generating a current parameter value set inresponse to a first control input, the current parameter value setproviding a pleasing or preferred listening experience for the wearer,and storing, in the non-volatile memory, the current parameter value setas a user-defined memory in the non-volatile memory.

Example Ex53. The method according to Ex52, comprising retrieving theuser-defined memory from the non-volatile memory in response to a secondcontrol input, and applying the parameter value set corresponding to theuser-defined memory to recreate the pleasing or preferred listeningexperience for the wearer.

Example Ex54. The method according to one or more of Ex27 to Ex53,comprising wherein applying, by the processor, one or more differentparameter value sets appropriate for the classification of the currentacoustic environment in response to one or more subsequently receivedcontrol input signals, learning, by the processor, wearer preferencesusing utilization data acquired during application of the differentparameter value sets applied by the processor, and adapting, by theprocessor, selection of subsequent parameter value sets by the processorfor subsequent use in the current acoustic environment using the learnedwearer preferences.

Example Ex55. The method according to one or more of Ex27 to Ex54,comprising applying, by the processor, one or more different parametervalue sets appropriate for the classification of the current acousticenvironment in response to one or more subsequently received controlinput signals, storing, by the processor in the memory, one or both ofutilization data and contextual data acquired by the processor duringapplication of the different parameter value sets associated with thecurrent acoustic environment, and adapting, by the processor, selectionof subsequent parameter value sets by the processor for subsequent usein the current acoustic environment using one or both of the utilizationdata and the contextual data.

Example Ex56. The method according to one or more of Ex27 to Ex55,wherein the processor is configured with instructions to implement amachine learning algorithm to one or more of automatically apply anadapted parameter value set appropriate for an initial or a subsequentclassification of the current acoustic environment, learn wearerpreferences using utilization data acquired during application of thedifferent parameter value sets applied by the processor, adapt selectionof subsequent parameter value sets for subsequent use in the currentacoustic environment using learned wearer preferences, and adaptselection of subsequent parameter value sets for subsequent use in thecurrent acoustic environment using one or both of utilization data andcontextual data.

FIGS. 1C and 1D illustrate an ear-worn electronic device 100 inaccordance with any of the embodiments disclosed herein. The hearingdevice 100 shown in FIGS. 1C and 1D can be configured to implement oneor more Mask Mode features disclosed herein. The hearing device 100shown in FIGS. 1C and 1D can be configured to implement one or more MaskMode features disclosed herein and one or more Edge Mode featuresdisclosed herein. The hearing device 100 shown in FIGS. 1C and 1D can beconfigured to include some or all of the components and/or functionalityof the hearing device 100 shown in FIGS. 1A and 1B.

The hearing device 100 shown in FIG. 1C differs from that shown in FIG.1A in that a control input 129 of, or operatively coupled to, theprocessor 120 is operatively coupled to a sensor-actuatable control 128in addition to the user-actuatable control 127. The hearing device 100shown in FIG. 1C includes a user interface comprising a user-actuatablecontrol 127 and a sensor-actuatable control 128 operatively coupled tothe processor 120 via a control input 129. The control input 129 isconfigured to receive a control input signal generated by one or both ofthe user-actuatable control 127 and the sensor-actuatable control 128.

The hearing device 100 shown in FIG. 1D differs from that shown in FIG.1A and FIG. 1C in that a control input 129 of, or operatively coupledto, the processor 120 is operatively coupled to a sensor-actuatablecontrol 128 and a communication device or devices 136, in addition tothe user-actuatable control 127. The hearing device 100 shown in FIG. 1Dincludes a user interface comprising the user-actuatable control 127,the sensor-actuatable control 128, and the communication device(s) 136,each of which is operatively coupled to the processor 120 via thecontrol input 129. The control input 129 is configured to receive acontrol input signal generated by one or more of the user-actuatablecontrol 127, the sensor-actuatable control 128, and the communicationdevice(s) 136. The communication device(s) 136 is configured tocommunicatively couple to an external electronic device 152 (e.g., asmartphone or a smart watch) and to receive a control input signal fromthe external electronic device 152. The control input signal istypically generated by the external electronic device 152 in response toan activation command initiated by the wearer of the hearing device 100.The control input signal received by the communication device(s) 136 iscommunicated to the control input 129 via the communication bus 121 or aseparate connection.

The hearing device 100 shown in FIGS. 1C and 1D can be configured toinclude a non-volatile memory 123 configured to store a multiplicity ofparameter value sets 125, each of the parameter value sets associatedwith a different acoustic environment and one or more Mask Modes. Thehearing device 100 shown in FIGS. 1C and 1D can be configured to includea non-volatile memory 123 configured to store a multiplicity ofparameter value sets 125, each of the parameter value sets associatedwith a different acoustic environment, one or more Mask Modes, and oneor more Edge Modes.

The user-actuatable control 127 is configured to receive an input fromthe wearer of the hearing device 100. The input from the wearer can beany type of user input, such as a touch input, a gesture input, or avoice input. The user-actuatable control 127 can include one or more ofa tactile interface, a gesture interface, and a voice command interface.The tactile interface can include one or more manually actuatableswitches (e.g., a push button, a toggle switch, a capacitive switch).For example, the user-actuatable control 127 can include a number ofmanually actuatable buttons or switches disposed on the hearing devicehousing 102. The user-actuatable control 127 can comprises a sensorresponsive to a touch or a tap (e.g., a double-tap) by the wearer. Theuser-actuatable control 127 can comprise a voice recognition controlimplemented by the processor 120. The user-actuatable control 127 can beresponsive to different types of wearer input. For example, an acousticenvironment adaptation feature of the hearing device 100 can beinitiated by a double-tap input followed by voice command and/orassistance thereafter.

The user-actuatable control 127 can comprise gesture detection circuitryresponsive to a wearer gesture made in proximity to the hearing device100 (e.g., a non-contacting gesture made spaced apart from the device).A single antenna and gesture detection circuitry of the hearing device100 can be used to classify wearer gestures, such as hand or fingermotions made in proximity to the hearing device. As the wearer's hand orfinger moves, the electrical field or magnetic field of the antenna isperturbed. As a result, the antenna input impedance is changed. When awearer performs hand or finger motions (e.g. waving, swipe, tap, holds,zoom, circular movements, etc.), an antenna impedance monitor recordsthe reflection coefficients of the signals or impedance. As the wearer'shand or finger moves, the changes in antenna impedance show uniquepatterns due to the perturbation of the antenna's electrical field ormagnetic field. These unique patterns can correspond to predetermineduser inputs, such as an input to implement an acoustic environmentadaptation feature of the hearing device 100. As will be discussed indetail hereinbelow, the user-actuatable control 127 is configured toreceive an input from the wearer of the hearing device 100 to initiatean acoustic environment adaptation feature of the hearing device 100.

The sensor-actuatable control 128 is configured to communicativelycouple to one or more external sensors 150. The sensor-actuatablecontrol 128 can include electronic circuitry to communicatively coupleto one or more external sensors 150 via a wireless connection or a wiredconnection. For example, the sensor-actuatable control 128 can includeone or more wireless radios (e.g., examples described herein) configuredto communicate with one or more sensors 150, such as a camera. Thecamera 150 can be a body-worn camera, such as a camera affixed toglasses worn by a wearer of the hearing device (e.g., a MyEye cameramanufactured by OrCam®). The camera 150 can be a camera of a smartphoneor a smart watch. In the context of activating a Mask Mode of thehearing device, the camera 150 can be configured to detect the presenceof a mask on the hearing device wearer and other persons within theacoustic environment. A processor of the camera 150 or an externalprocessor (e.g., one or more of a remote processor, a cloudserver/processor, a smartphone processor, a smart watch processor) canimplement mask recognition software to detect the presence of a mask,the type of mask, the mask manufacturer, and/or the mask material.

For example, mask recognition software implemented by one or more of theaforementioned processors can be configured to identify the followingtypes of masks: a homemade cloth mask, a bandana, a T-shirt mask, astore-bought cloth mask, a cloth mask with filter, a neck gaiter, abalaclava, a disposable surgical mask, a cone-style mask, an N95 mask,and a respirator. In some implementations, the mask recognition softwarecan detect the type, manufacturer, and model of the masks within theacoustic environment. Each of these (and other) mask types can have anassociated parameter value set 125 stored in non-volatile memory 123 ofthe hearing device 100. In some embodiments, mask-related data of theparameter value sets 125 can be received from a smartphone/smart watchor cloud server and integrated into the parameter value sets 125 storedin non-volatile memory 123. In response to performing mask recognitionfor each mask within the acoustic environment, the processor 120 of thehearing device 100 can select and apply a parameter value set 125appropriate for the acoustic environment classification and each of thedetected masks within the acoustic environment.

As previously discussed, the control input 129 of hearing device 100shown in FIG. 1D is operatively coupled to the communication device(s)136 and is configured to receive a control input signal from an externalelectronic device 152, such as a smartphone or a smartwatch. In responseto receiving the control input signal from the external electronicdevice 152, the processor 120 is configured to initiate an acousticenvironment adaptation feature of the hearing device 100, such as byinitiating one or more both of an Edge Mode and a Mask Mode of thehearing device 100.

In some embodiments, the hearing device 100 shown in FIGS. 1C and 1D caninclude a sensor arrangement 134. The sensor arrangement 134 can includeone or more sensors configured to sense one or more of a physical state,a physiologic state, and an activity status of the wearer and to producesensor signals. The sensor arrangement 134 can include one or more ofthe sensors discussed previously with reference to FIG. 1A.

The hearing device 100 shown in FIGS. 1C and 1D can also include aclassification module 138 operably coupled to the processor 120. Theclassification module 138 can be implemented in software, hardware, or acombination of hardware and software, and in a manner previouslydescribed with reference to FIG. 1A.

As previously discussed, the classification module 138 can be configuredto detect different types of sound and different types of acousticenvironments. The different types of sound can include speech, music,and several different types of noise (e.g., wind, transportation noiseand vehicles, machinery), etc., and combinations of these and othersounds (e.g., transportation noise with speech). The different types ofacoustic environments can include a moderately loud restaurant, quietrestaurant speech, large room speech, sports stadium, concertauditorium, etc. Speech can include clean speech, noisy speech, andmuffled speech delivered by masked speakers/persons. Clean speech cancomprise speech spoken by different persons at different reverberationsituations, such as a living room or a cafeteria. Muffled speech cancomprise speech spoken by different persons speaking through a mask atdifferent reverberation situations, such as a conference room or anairport. Noisy speech (e.g., speech with noise) can be clean speech ormuffled speech mixed randomly with noise (e.g., noise at three levels ofSNR: −6 dB, 0 dB and 6 dB). Machine noise can contain noise generated byvarious machineries, such as an automobile, a vacuum and a blender.Other sound types or classes can include any sounds that are notsuitably described by other classes, for instance the sounds from waterrunning, foot stepping, etc.

In various embodiments, the classification module 138 can be configuredto classify sound sensed by the microphone(s) 130 as one of music,speech (e.g., clear, muffled, noisy), and non-speech. The non-speechsound classified by the classification module 138 can include one ofmachine noise, wind noise, and other sounds. According to variousembodiments, and as disclosed in commonly-owned U.S. Published PatentApplication Serial No. 2011/0137656 which is incorporated herein byreference, the classification module 138 can comprise a feature sethaving a number of features for sound classification determined based onperformance and computational cost of the sound classification. In someimplementations, for example, the feature set can comprise 5 to 7features, such as Mel-scale Frequency cepstral coefficients (MFCC). Inother implementations, the feature set can comprise low level features.

FIG. 8 illustrates a method of implementing a user-initiated, asensor-initiated, and/or an external electronic device-initiatedacoustic environment adaptation feature of an ear-worn electronic devicein accordance with any of the embodiments disclosed herein. The methodshown in FIG. 8 involves storing 802 a plurality of parameter value setsin non-volatile memory of the ear-worn electronic device. Each of theparameter value sets is associated with a different acousticenvironment, wherein at least one or more of the parameter value setsare associated with an acoustic environment with muffled speechdelivered by one or more masked persons within the acoustic environment.The method involves sensing 804 sound in an acoustic environment usingone or more microphones of the hearing device. The method also involvesclassifying 806, by a processor of the hearing device using the sensedsound, the acoustic environment as one with muffled speech.

The method further involves receiving 808 a signal from a control inputof the hearing device. The control input signal can be generated by auser-actuatable control, a sensor-actuatable control, or an externalelectronic device communicatively coupled to a communication device ofthe hearing device. The method also involves applying 810, by theprocessor in response to the control input signal, one or more of theparameter value sets appropriate for the classification to enhanceintelligibility of muffled speech.

In accordance with any of the embodiments disclosed herein, and asadditional processing steps to the method illustrated in FIG. 8, themethod can additionally involve determining, by the processor, anactivity status of the wearer. The method can also involve applying, bythe processor, one or more of the parameter value sets appropriate forthe classification (e.g., a classification involving muffled speech) andthe activity status in response to the control input signal.

According to any of the embodiments disclosed herein, and as additionalprocessing steps to the method illustrated in FIG. 8, the method canadditionally involve sensing, using a sensor arrangement, one or more ofa physical state, a physiologic state, and an activity state of thewearer and producing signals by the sensor arrangement. The method canalso involve applying, by the processor, one or more of the parametervalue set appropriate for the classification (e.g., a classificationinvolving muffled speech) in response to the control input signal andthe sensor signals.

By way of example, the wearer may be sitting alone in a moderately loudcafé and engaged in reading a newspaper. According to the methodsdiscussed above, the processor of the wearer's hearing device wouldclassify the acoustic environment generally as a moderately loudrestaurant. In the case of masked persons being present, the processorwould classify the acoustic environment generally as a moderately loudrestaurant with masked speakers.

In addition, the processor would receive sensor signals from a sensorarrangement of the hearing device which provide an indication of thewearer's physical state, the physiologic state, and/or activity statuswhile present in the current acoustic environment. In this illustrativeexample, a motion sensor could sense relatively little or minimal heador neck movement indicative of reading rather than speaking with atablemate at the café. The processor could also sense the absence ofspeaking by the wearer and/or a nearby person in response to signalsproduced by the microphone(s) of the hearing device. The additionalinformation provided by the sensor arrangement of the hearing deviceprovides contextual or listening intent information which can be used bythe processor to refine the acoustic environment classification.

For example, without the additional sensor information, the processorwould configure the hearing device for operation in an acousticenvironment classified as “quiet restaurant speech.” This classificationwould assume that the wearer is engaged in conversation with anotherperson (e.g., masked or non-masked) within a quiet restaurantenvironment, which would not be accurate. In response to determiningthat the wearer is not engaged in conversation based on sensor signalsreceived from the sensor arrangement, the processor of the hearingdevice would refine the acoustic environment classification as “quietrestaurant non-speech” or “quiet restaurant reading,” which would bereflective of the listener's intent within the current acousticenvironment.

FIG. 9 illustrates a method of implementing a user-initiated, asensor-initiated, and/or an external electronic device-initiatedacoustic environment adaptation feature of an ear-worn electronic devicein accordance with any of the embodiments disclosed herein. The methodshown in FIG. 9 involves storing 902 parameter value sets including aNormal Parameter Value Set in non-volatile memory (NVM) of an ear-wornelectronic device. Each of the other parameter value sets is associatedwith a different acoustic environment including an acoustic environmentor environments with muffled speech and defining offsets to parametersof the Normal Parameter Value Set.

The method involves moving/storing 904 the Normal Parameter Value Setfrom/in NVM to main memory of the device. The method also involvessensing 906 sound in an acoustic environment using one or moremicrophones of the device. The method further involves classifying 908,by a processor of the device using the sensed sound, the acousticenvironment as one with muffled speech. The method also involvesreceiving 910 a signal from a control input of the hearing device. Thecontrol input signal can be generated by a user-actuatable control, asensor-actuatable control, or an external electronic devicecommunicatively coupled to a communication device of the hearing device.The method further involves applying 912 offsets of the selectedparameter value set to parameters of the Normal Parameter Value Setresiding in main memory appropriate for the classification to enhanceintelligibility of muffled speech.

FIG. 10 illustrates various types of parameter value set data that canbe stored in non-volatile memory in accordance with any of theembodiments disclosed herein. The non-volatile memory 1000 shown in FIG.10 can include parameter value sets 1010 for different acousticenvironments, including various acoustic environments with muffledspeech (e.g., Acoustic Environments A, B, C, . . . N). The non-volatilememory 1000 can include parameter value sets 1020 for differentmask-wearing speakers, including the wearer of the hearing device(masked device wearer), masked persons known the hearing device wearer(e.g., family members, friends, business colleagues—masked persons A-N),and/or a population of mask wearers (e.g., averaged parameter value set,such as average gain values or gain offsets). The non-volatile memory1000 can include parameter value sets 1030 specific for different typesof masks (see examples above). For example, parameter value set A can bespecific for a cloth mask, parameter value set B can be specific for acloth mask with filter, parameter value set C can be specific for adisposable surgical mask, parameter value set D can be specific for anN95 mask, and parameter value set N can be specific for a genericrespirator.

FIG. 11 illustrates a process of implementing a user-initiated, asensor-initiated, and/or an external electronic device-initiatedacoustic environment adaptation feature of an ear-worn electronic devicein accordance with any of the embodiments disclosed herein. According tothe process shown in FIG. 11, the acoustic environment adaptationfeature is initiated in response to receiving 1100 a control inputsignal at a control input of the hearing device. The control inputsignal can be generated by a user-actuatable control, asensor-actuatable control, or an external electronic devicecommunicatively coupled to a communication device of the hearing device.Prior to or after receiving the control input signal, an acousticsnapshot of the listening environment is read or interpreted 1102 by thehearing device. In some implementations, the hearing device can beconfigured to continuously or repetitively (e.g., every 11, 10, or 30seconds) sense and classify the acoustic environment prior receiving thecontrol input signal. In other implementations, the hearing device canbe configured to classify the acoustic environment in response toreceiving the control input signal (e.g., after actuation of theuser-actuated control or the sensor-actuated control). An acousticsnapshot is generated by the hearing device based on the classificationof the acoustic environment. After reading or interpreting 1102 theacoustic snapshot, the method involves looking up 1104 parameter valuechanges (e.g., offsets) stored in non-volatile memory of the hearingdevice. The method also involves applying 1106 parameter value changesto the hearing device.

The processes shown in FIG. 11 can be initiated and repeated on an“on-demand” basis by the wearer by actuating the user-actuatable controlof the hearing device or by generating a control input signal via anexternal electronic device communicatively coupled to the hearingdevice. Alternatively or additionally, the processes shown in FIG. 11can be initiated and repeated on a “sensor-activated” basis in responseto a control input signal generated by an external device or sensor(e.g., a camera or other sensor) communicatively coupled to the hearingdevice. This on-demand/sensor-activated capability allows the hearingdevice to be quickly (e.g., instantly or immediately) configured foroptimal performance in the wearer's current acoustic environment (e.g.,an acoustic environment with muffled speech) and in accordance with thewearer's listening intent. In contrast, conventional fully-autonomoussound classification techniques implemented in hearing devices providefor slow and gradual adaptation to the wearer's current acousticenvironment. Moreover, conventional fully-autonomous soundclassification techniques do not always provide desirable sound and canbe distracting when the wearer is in a dynamic acoustic environment andthe adaptations occur frequently.

FIG. 12 illustrates a processor and non-volatile memory of an ear-wornelectronic device configured to implement a user-initiated, asensor-initiated, and/or an external electronic device-initiatedacoustic environment adaptation feature in accordance with any of theembodiments disclosed herein. FIG. 12 illustrates additional details ofthe processes of the method shown in FIGS. 8 and 9 and other methodfigures. The processor 1210 is operably coupled to non-volatile memory1202 which is configured to store a number of lookup tables 1204, 1206.

Lookup table 1204 includes a table comprising a plurality of differentacoustic environment classifications 1204 a (AEC₁-AEC_(N)). Anon-exhaustive, non-limiting list of different acoustic environmentclassifications 1204 a can include, for example, any one or anycombination of speech in quiet, speech in babble noise, speech in carnoise, speech in noise, muffled speech in quiet, muffled speech inbabble noise, muffled speech in car noise, muffled speech in noise, carnoise, wind noise, machine noise, and other noise. Each of the acousticenvironment classifications 1204 a has associated with it a set ofparameter values 1204 b (PV₁-PV_(N)) and a set of device settings 1204 c(DS₁-DS_(N)). The parameter value sets 1204 b (PV₁-PV_(N)) can include,for example, a set of gain values or gain offsets associated with eachof the different acoustic environment classifications 1204 a(AEC₁-AEC_(N)). The device settings 1204 c (DS₁-DS_(N)) can include, forexample, a set of noise-reduction parameters associated with each of thedifferent acoustic environment classifications 1204 a (AEC₁-AEC_(N)).The device settings 1204 c (DS₁-DS_(N)) can also include, for example, aset of microphone mode parameters (e.g., omni mode, directional mode)associated with each of the different acoustic environmentclassifications 1204 a (AEC₁-AEC_(N)).

Lookup table 1206 includes a lookup table associated with each of anumber of different sensors of the hearing device. In the illustrativeexample shown in FIG. 12, the lookup table 1206 includes table 1206-1associated with Sensor A (e.g., an IMU). Sensor A is characterized tohave a plurality of different sensor output states (SOS) 1206-1 a(SOS₁-SOS_(N)) of interest. Each of the sensor output states 1206-1 ahas associated with it a set of parameter values 1206-1 b (PV₁-PV_(N))and a set of device settings 1206-1 c (DS₁-DS_(N)). The lookup table1206 also includes table 1206-N associated with Sensor N (e.g., aphysiologic sensor). Sensor N is characterized to have a plurality ofdifferent sensor output states 1206-Na (SOS₁-SOS_(N)) of interest (e.g.,an IMU can have sensor output states of sitting, standing, lying down,running, walking, etc.). Each of the sensor output states 1206-Na hasassociated with it a set of parameter values 1206-Nb (PV₁-PV_(N)) and aset of device settings 1206-Nc (DS₁-DS_(N)).

The parameter value sets 1206-1 b, 1206-Nb (PV₁-PV_(N)) can include, forexample, a set of gain values or gain offsets associated with each ofthe different sensor output states 1206-1 a (SOS₁-SOS_(N)). The devicesettings 1206-1 c, 1206-Nc (DS₁-DS_(N)) can include, for example, a setof noise-reduction parameters associated with each of the differentsensor output states 1206-Na (SOS₁-SOS_(N)). The device settings 1206-1c, 1206-Nc (DS₁-DS_(N)) can also include, for example, a set ofmicrophone mode parameters (e.g., omni mode, directional mode)associated with each of the different sensor output states 1206-1 a,1206-Na.

The processor 1210 of the hearing device, in response to sensing soundin an acoustic environment using one or more microphones, is configuredto classify the acoustic environment using the sensed sound. Havingclassified the sensed sound, the processor 1210 performs a lookup intable 1204 to obtain the parameter value set 1204 b and device settings1204 c that correspond to the acoustic environment classification 1204a. Additionally, the processor 1210 performs a lookup in table 1206 inresponse to receiving sensor signals from one or more sensors of thehearing device. Having received sensor signals indicative of an outputstate of one or more hearing device sensors, the processor 1210 obtainsthe parameter value set 1206-1 b, 1206-Nb and device settings 1206-1 c,1206-Nc that correspond to the sensor output state 1206-1 a, 1206-Na.

After performing lookups in tables 1204 and 1206, the processor 1210 isconfigured to select 1212 parameter value sets and device settingsappropriate for the acoustic environment and the received sensorinformation. The main memory (e.g., custom or active memory) of thehearing device is updated 1214 in a manner previously described usingthe selected parameter value sets and device settings. Subsequently, theprocessor 1210 processes sound using the parameter value settings anddevice setting residing in the main memory. It is understood that, inless complex implementations, the non-volatile memory 1202 can excludelookup table 1206, and the hearing device can be configured to implementa user-initiated, a sensor-initiated, and/or an external electronicdevice-initiated acoustic environment adaptation feature using lookuptable 1204.

The following features can be implemented by a hearing device inaccordance with any of the embodiments disclosed herein. With continuedreference to FIG. 12 for purposes of example, the processor 1210 can beconfigured to apply a first parameter value set (e.g., PV1) to enhanceintelligibility of muffled speech uttered by the wearer of the hearingdevice, and apply a second parameter value set (e.g., PV2), differentfrom the first parameter value set (e.g., PV1), to enhanceintelligibility of muffled speech uttered by one or more persons otherthan the wearer of the hearing device. For example, the first and secondparameter value sets can be swapped in and out of main memory 1214during a conversation between a masked hearing device wearer and thewearer's masked friend to improve the intelligibility of speech utteredby the wearer and the wearer's friend.

The processor 1210 can be configured to classify the acousticenvironment and detect a change in gain for frequencies within aspecified frequency range relative to a baseline in response toreceiving a control input signal at the control input 1211, wherein thechange in gain is indicative of the presence of muffled speech. Theprocessor 1210 can be configured to continuously or repetitivelyclassify the acoustic environment to monitor for a change in gain forfrequencies within a specified frequency range relative to a baselineprior to receiving a control input signal at the control input 1211,wherein the change in gain is indicative of the presence of muffledspeech. The baseline can comprise a generic baseline associated with apopulation of mask-wearing persons not known by the wearer. The baselinecan comprise a baseline associated with one or more specified groups ofmask-wearing persons known to the wearer (e.g., family, friends,colleagues).

The parameter value sets associated with an acoustic environment withmuffled speech can comprise a plurality of parameter value sets (e.g.,PV₅-PV₁₀) each associated with a different type of mask wearable by theone or more masked persons, including the masked hearing device wearer.Each of the parameter value sets can comprise a set of gain values orgain offsets associated with a different acoustic environment (e.g.,AE₁-AE_(N) associated with AEC₁-AEC_(N)), and the processor 1210 can beconfigured to increase the set of gain values or gain offsets for aspecified frequency range in response to classifying the acousticenvironment as one with muffled speech. The specific frequency rangediscussed herein can comprise a frequency range of about 0.5 kHz toabout 4 kHz.

Each of the parameter value sets can comprise a set of gain values orgain offsets associated with a different acoustic environment (e.g.,AE₁-AE_(N) associated with AEC₁-AEC_(N)) and a set of noise-reductionparameters (e.g., DS₁-DS_(N)) associated with the different acousticenvironments. Each of the parameter value sets can comprise a set ofgain values or gain offsets associated with a different acousticenvironment (e.g., AE₁-AE_(N) associated with AEC₁-AEC_(N)), a set ofnoise-reduction parameters (e.g., DS₁-DS_(N)) associated with thedifferent acoustic environments, and a set of microphone mode parameters(e.g., DS₁-DS_(N)) associated with the different acoustic environments.

The parameter value sets (e.g., PV₁-PV_(N)) can comprise a normalparameter value set associated with a normal or default acousticenvironment and a plurality of other parameter value sets eachassociated with a different acoustic environment including one or moreparameter value sets associated with an acoustic environment withmuffled speech. Each of the other parameter value sets can defineoffsets to parameters of the normal parameter value set.

FIG. 13 illustrates a method of implementing a user-initiated, asensor-initiated, and/or an external electronic device-initiatedacoustic environment adaptation feature of an ear-worn electronic devicein accordance with any of the embodiments disclosed herein. The methodshown in FIG. 13 can be implemented alone or in combination with any ofthe methods and processes disclosed herein. The method shown in FIG. 13involves automatically generating 1302, during use of an ear-wornelectronic device, a current parameter value set associated with acurrent acoustic environment with one or both of muffled speech andnon-muffled speech. The current parameter value set can be one thatprovides a pleasant or preferred listening experience for the wearer ofthe ear-worn electronic device within the current acoustic environment.

The method involves storing 1304, in non-volatile memory of the ear-wornelectronic device, the current parameter value set as a User-DefinedMemory in the non-volatile memory. The method also involves retrieving1306 the User-Defined Memory from the non-volatile memory in response toa second control input. The method further involves applying 1308 theparameter value set corresponding to the User-Defined Memory to recreatethe pleasing or preferred listening experience for the wearer.

It is understood that, in the context of ear-worn electronic devicessuch as hearing aids, the term “memories” (e.g., the User-Defined Memoryof FIG. 13) refers generally to a set of parameter settings (e.g.,parameter value sets, device settings) that are stored in long-term(e.g., non-volatile) memory of an ear-worn electronic device. One ormore of these memories can be recalled by a wearer of the ear-wornelectronic device (or automatically/semi-automatically by the ear-wornelectronic device) as desired and applied by a processor of the ear-wornelectronic device to provide a particular listening experience for thewearer.

In some embodiments, the method illustrated in FIG. 13 (and in otherfigures) can be implemented with the assistance of a smartphone or otherpersonal digital assistant (e.g., a smart watch, tablet or laptop). Forexample, and with reference to FIGS. 14A-14C, a smartphone 1400 canstore and execute an app configured to facilitate connectivity andinteraction with an ear-worn electronic device of a type previouslydescribed. The app executed by the smartphone 1400 allows the wearer todisplay the current listening mode (e.g., Edge Mode, Mask Mode, othermode), which in the case of FIG. 14A is an Edge Mode. As can be seen onthe display of the smartphone 1400 in FIG. 14A, Edge Mode is indicatedas currently active. Although FIGS. 14A-14C illustrate smartphonefeatures associated with Edge Mode, it is understood that these figuresand corresponding functions are equally applicable to smartphonefeatures associated with Mask Mode. In other words, the term Edge Modein FIGS. 14A-14C can be replaced by the term Mask Mode.

With Edge Mode (or Mask Mode) active, the wearer can perform a number offunctions, such as Undo, Try Again, and Create New Favorite functions ascan be seen on the display of the smartphone 1400 in FIG. 14B. Thewearer can tap on the ellipses and choose one of the various availablefunctions. For example, the wearer can tap on the Create New Favoriteicon to create a User-Defined Memory. Tapping on the Create New Favoriteicon shown in FIG. 14B causes a Favorites display to be presented, ascan be seen in FIG. 14C. The wearer can press the Add icon to create anew User-Defined Memory. The wearer is prompted to name the newUser-Defined Memory, which is added to the Favorite menu (which can beactivated using the Star icon on the home page shown in FIG. 14A).

As can be seen in FIG. 14C, a number of different User-Defined Memoriescan be created by the wearer, each of which can be named by the wearer.A number of predefined memories can also be made available to the wearervia the Favorites page. The User-Defined Memories and/or predefinedmemories can be organized based on acoustic environment, such as Home,Office, Restaurant, Outdoors, and Custom (wearer-specified)environments. In some implementations, the last three temporary states(Edge Mode or Mask Mode attempts) are kept and the wearer user can tapon the ellipses next to one of those labels under the Recent heading andconvert that to a Favorite.

FIG. 15 illustrates a processor, a machine learning processor, and anon-volatile memory of an ear-worn electronic device configured toimplement an acoustic environment adaptation feature in accordance withany of the embodiments disclosed herein. The components andfunctionality shown and described with reference to FIG. 15 can beincorporated and implemented in any of the hearing devices disclosedherein (e.g., see FIGS. 1A-1D, 7, 10, 12). The processes described withreference to FIG. 15 can be processing steps of any of the methodsdisclosed herein (e.g., see FIGS. 2-6, 8, 9, 11, and 13).

FIG. 15 shows various components of a hearing device 100 in accordancewith any of the embodiments disclosed herein. The hearing device 100includes a processor 120 (e.g., main processor) coupled to a memory 122,a non-volatile memory 123, and a communication device 136. Thesecomponents of the hearing device 100 can be of a type and have afunctionality previously described. The processor 120 is operativelycoupled to a machine learning processor 160. The machine learningprocessor 160 is configured to execute computer code or instructions(e.g., firmware, software) including one or more machine learningalgorithms 162. The machine learning processor 160 is configured toreceive and process a multiplicity of inputs 170 and generate amultiplicity of outputs 180 via one or more machine learning algorithms162. The machine learning processor 160 can be configured to processand/or generate various internal data using the input data 170, such asone or more of utilization data 164, contextual data 166, and adaptationdata 168. The machine learning processor 160 generates, via the one ormore machine learning algorithms 162, various outputs 180 using thesedata.

The machine learning processor 160 can be configured with executableinstructions to process one or more of the inputs 170 and generate oneor more of the outputs 180 shown in FIG. 15 and other figures via aneural network and/or a support vector machine (SVM). The neural networkcan comprise one or more of a deep neural network (DNN), a feedforwardneural network (FNN), a recurrent neural network (RNN), a longshort-term memory (LSTM), gated recurrent units (GRU), light gatedrecurrent units (LiGRU), a convolutional neural network (CNN), and aspiking neural network.

An acoustic environment adaptation feature of the hearing device 100 canbe initiated by a double-tap input followed by voice commands uttered bythe wearer and/or voice assistance provided by the hearing device 100.Alternatively, or additionally, an acoustic environment adaptationfeature can be initiated via a control input signal generated by anexternal electronic device. A voice recognition facility of the hearingdevice 100 can be configured to listen for voice commands, keywords(e.g., performing keyword spotting), and key phrases uttered by thewearer after initiating the acoustic environment adaptation feature. Themachine learning processor 162, in cooperation with the voicerecognition facility, can be configured to ascertain/identify the intentof a wearer's voice commands, keywords, and phrases and, in response,adjust the acoustic environment adaptation to more accurately reflectthe wearer's intent. For example, the machine learning processor 160 canbe configured to perform keyword spotting for various pre-determinedkeywords and phrases, such as “activate [or deactivate] Edge Mode” and“activate [or deactivate] Mask Mode.”

FIG. 15 shows a representative set of inputs 170 that can be receivedand processed by the machine learning processor 160. The inputs 170 caninclude wearer inputs 171 (e.g., via a user-interface of the hearingdevice 100), external electronic device inputs 172 (e.g., via asmartphone or smartwatch), one or more sensor inputs 174 (e.g., via amotion sensor and/or one or more physiologic sensors), microphone inputs175 (e.g., acoustic environment sensing, wearer voice commands), andcamera inputs 176 (e.g., for detecting masked persons in the acousticenvironment). The inputs 170 can also include test mode inputs 178(e.g., random variations of selected hearing device parameters 182, 184,186) which can cause the hearing device 100 to strategically andautomatically make various hearing device adjustments/adaptations toevaluate the wearer's acceptance or non-acceptance of suchadjustments/adaptations. For example, the machine learning processor 160can learn how long a wearer stays in a particular setting during a testmode. Test mode data can be used to fine-tune the relationship betweennoise and particular parameters. The test mode inputs 178 can be used tofacilitate automatic enhancement (e.g., optimization) of an acousticenvironment adaptation feature implemented by the hearing device 100.

The outputs 180 from the machine learning processor 160 can includeidentification and selection of one or more parameter value sets 182,one or more noise-reduction parameters 184, and/or one or moremicrophone mode parameters 186 that provide enhanced speechintelligibility and/or a more pleasing listening experience. Theparameter value sets 182 can include one or both of predefined parametervalue sets 183 (e.g., those established using fitting software at thetime of hearing device fitting) and adapted parameter value sets 185.The adapted parameter value sets 185 can include parameter value setsthat have been adjusted, modified, refined or created by the machinelearning processor 160 via the machine learning algorithms 162 operatingon the various inputs 170 and/or various data generated from the inputs170 (e.g., utilization data 164, contextual data 166, adaptation data168).

The utilization data 164 generated and used by the machine learningprocessor 160 can include how frequently various modes of the hearingdevice (e.g., Edge Mode, Mask Mode) are utilized. For example, theutilization data 164 can include the amount of time the hearing device100 is operated in the various modes and the acoustic classification forwhich each mode is engaged and operative. The utilization data 164 canalso include wearer behavior when switching between various modes, suchas how the wearer switches from a specific adaptation to a differentadaptation (e.g., timing of mode switching; mode switching patterns).

Contextual data 166 can include contextual and/or listening intentinformation which can be used by the machine learning processor 160 aspart of the acoustic environment classification process and to adapt theacoustic environment classification to more accurately track thewearer's contextual or listening intent. Sensor, microphone, and/orcamera input signals can be used by the machine learning processor 162to generate contextual data 166, which can be used alone or togetherwith the utilization data 164 to ascertain and identify the intent ofthe wearer when adapting the acoustic environment classification featureof the hearing device 100. These input signals can be used by themachine learning processor 160 to determine the contextual factors thatcaused or cause the wearer to initiate acoustic environment adaptationsand changes to such adaptations. The input signals can include motionsensor signals, physiologic sensor signals, and/or microphone signalsindicative of sound in the acoustic environment.

For example, motion sensor signals can be used by the machine learningprocessor 162 ascertain and identify the activity status of the wearer(e.g., walking, sitting, sleeping, running). By way of example, a motionsensor of the hearing device 100 can be configured to detect changes inwearer posture which can be used by the machine learning processor 160to infer that the wearer is changing environments. For example, themotion sensor can be configured to detect changes between sitting andstanding, from which the machine learning processor 160 can infer thatthe acoustic environment is or will soon be changing (e.g., detecting achange from sitting in a car to walking from the car into a store;detecting a change from lying down to standing and walking into anotherroom). Microphone and/or camera input signals can be used by the machinelearning processor 160 to corroborate the change in wearer posture oractivity level detected by the motion sensor.

In another example, the microphone input signals can be used by themachine learning processor 162 to determine whether the wearer isengaged in conversation (e.g., interactive mode) or predominantlyengaged in listening (e.g., listening to music at a concert or to aperson giving a speech). The microphone input signals can be used by themachine learning processor 162 to determined how long (e.g., apercentage or ratio) the wearer is using his or her own voice relativeto other persons speaking (or the wearer listening) by implementing an“own voice” algorithm. The microphone input signals can also be used bythe machine learning processor 162 to determine whether a “significantother” is speaking by implementing a “significant other voice”algorithm. The microphone input signals can be used by the machinelearning processor 162 to detect various characteristics of the acousticenvironment, such as noise sources, reverberation, and vocal qualitiesof speakers. Using the microphone input signals, the machine learningprocessor 160 can be configured to select one or more of a parametervalue set 182, noise reduction parameters 184, and/or microphone modeparameters 186 best suited for the wearer's current acousticenvironment/mode (e.g., interactive or listening; own voice; significantother speaking; noisy).

The machine learning processor 160 is configured to learn wearerpreferences using the utilization data 164 and/or the contextual data166, and to generate adaptation data 168 in response to learning thewearer preferences. The adaptation data 168 can be used by the machinelearning processor 160 to select one or more of a parameter value set182, noise reduction parameters 184, and/or microphone mode parameters186 best suited for the wearer's current acoustic environment/mode.

For example, the machine learning processor 160 can be configured toapply an initial parameter value set 182 (e.g., a predefined parametervalue set 183) appropriate for an initial classification of an acousticenvironment in response to receiving an initial control input signalfrom the wearer or the wearer's smartphone or smart watch, for example.The machine learning processor 160, subsequent to applying the initialparameter value set, can be configured to automatically apply an adaptedparameter value set 185 appropriate for the initial or a subsequentclassification of the current acoustic environment in the absence ofreceiving a subsequent control input signal from the wearer or thewearer's smartphone or smart watch.

In another example, the machine learning processor 160 can be configuredto apply one or more different parameter value sets 182 appropriate forthe classification of the current acoustic environment in response toone or more subsequent control input signals received from the wearer orthe wearer's smartphone or smart watch, for example. The machinelearning processor 160 can be configured learn wearer preferences usingutilization data 164 and/or contextual data 166 acquired duringapplication of the different parameter value sets 182 by the machinelearning processor 160, and adapt selection of subsequent parametervalue sets 182 by the machine learning processor 160 for subsequent usein the current acoustic environment using the learned wearerpreferences.

In a further example, the machine learning processor 160 can beconfigured apply one or more different parameter value sets 182appropriate for the classification of the current acoustic environmentin response to one or more subsequent control input signals receivedfrom wearer or the wearer's smartphone or smart watch, for example. Themachine learning processor 160 can be configured to store, in a memory,one or both of utilization data 164 and contextual data 166 acquired bythe machine learning processor 160 during application of the differentparameter value sets associated with the current acoustic environment.The machine learning processor 160 can be configured to adapt selectionof subsequent parameter value sets 182 by the machine learning processor160 for subsequent use in the current acoustic environment using one orboth of the utilization data 164 and the contextual data 166.

In another example, the machine learning processor 160 can be configuredto one or more of automatically apply an adapted parameter value setappropriate for an initial or a subsequent classification of the currentacoustic environment, learn wearer preferences using utilization data164 and/or contextual data 166 acquired during application of thedifferent parameter value sets 182 applied by the machine learningprocessor 160, adapt selection of subsequent parameter value sets 182 bythe machine learning processor 160 for subsequent use in the currentacoustic environment using learned wearer preferences, and adaptselection of subsequent parameter value sets 182 for subsequent use inthe current acoustic environment using one or both of utilization data164 and contextual data 166.

After having learned preferences of the wearer, the machine learningprocessor 160 can implement other processes, such as changing memories,re-adapting selection of parameter value sets 182, repeating thisprocess to refine selection of parameter value sets 182, and turning onand off the dynamic adaptation feature implemented by the hearing device100. The machine learning processor 160 can be configured to learn inputsignals from various sources that are associated with a change inacoustic environment, which may trigger a dynamic adaptation event. Themachine learning processor 160 can be configured to adjust hearingdevice settings to improve sound quality and/or speech intelligibility,and to achieve an improved or optimal between comfort (e.g., noiselevel) and speech intelligibility. For example, the machine learningprocessor 160 can implement various frequency filters to reduce noisesources depending on the classification of the current acousticenvironment.

In some configurations, the machine learning processor 160 can beconfigured to provide separately adjustable compression pathways forsound received by a microphone arrangement of the hearing device 100.For example, the machine learning processor 160 can be configured toinput an audio signal to a fast signal level estimator (fast SLE) havinga fast low-pass filter characterized by a rise time constant and a decaytime constant. The machine learning processor 160 can be configured toinput the audio signal to a slow signal level estimator (slow SLE)having a slow low-pass filter characterized by a rise time constant anda decay time constant. The rise time constant and the decay timeconstant of the fast low-pass filter can both be between 1 millisecondand 10 milliseconds, and the rise time constant and the decay timeconstant of the slow low-pass filter can both be between 100milliseconds and 1000 milliseconds.

The machine learning processor 160 can be configured to subtract theoutput of the slow SLE from the output of the fast SLE and input theresult to a fast level-to-gain transformer. The machine learningprocessor 160 can be configured to input the output of the slow SLE to aslow level-to-gain transformer, wherein the slow level-to-gaintransformer is characterized by expansion when the output of the slowSLE is below a specified threshold. The machine learning processor 160can be configured to amplify the audio signal with a gain adjusted by asummation of the outputs of the fast level-to-gain transformer and theslow level-to-gain transformer, wherein the output of the fastlevel-to-gain transformer is multiplied by a weighting factor computedas a function of the output of the slow SLE before being summed with theoutput of the slow level-to-gain transformer. The hearing device 100 canbe configured to provide for separately adjustable compression pathwaysfor sound received by the hearing device 100 in manners disclosed incommonly-owned U.S. Pat. No. 9,408,001, which is incorporated herein byreference.

The machine learning processor 160 can be configured to implementhigh-speed adaptation of the wearer's listening experience based onwhether the wearer is speaking or listening and/or for each of amultiplicity of speakers in an acoustic environment. For example, adifferent adaptation can be implemented by the machine learningprocessor 160 when the wearer is speaking and when the wearer islistening. An adaptation implemented by the machine learning processor160 can be selected to reduce occlusion of the wearer's own voice whenspeaking (e.g., reduce low frequencies). The machine learning processor160 can be configured to turn on or off “own voice” and/or “significantother voice” algorithms. In some configurations, the machine learningprocessor 160 can be configured to implement parallel processing byrunning multiple adaptations simultaneously and dynamically choosingwhich of the multiple adaptations is implemented (e.g., gait using “ownvoice” determination).

The machine learning processor 160 can be configured to implementhigh-speed adaptation of the wearer's listening experience based on eachof a multiplicity of speakers in an acoustic environment. For example,the machine learning processor 160 can analyze the acoustic environmentfor a relatively short period of time (e.g., one or two minutes) inorder to identify different speakers in the acoustic environment. For agiven window of time, the machine learning processor 160 can identifythe speakers present during the time window. Based on the identifiedspeakers and other characteristics of the acoustic environment, themachine learning processor 160 can switch the acoustic environmentadaptation based on the number of speakers and thequality/characteristics of their voices (e.g., pitch, frequency).

In accordance with any of the embodiments disclosed herein, dataconcerning wearer utilization of various hearing device modes (e.g.,Edge Mode, Mask Mode), acoustic environment classification andadaptations, and other data received and produced by the machinelearning processor 160 and the processor 120 of the hearing device 100can be communicated to an external electronic device or system via thecommunication device 136. For example, these data can be communicatedfrom the hearing device 100 to a smart charger 190 configured to chargea rechargeable power source of the hearing device 100, typically on anightly basis. The data transferred from the hearing device 100 to thesmart charger 190 can be communicated to a cloud server 192 (e.g., viathe Internet). These data can be transferred to the cloud server 192 ona once-per-day basis.

The data received by the cloud server 192 can be used by a processor ofthe cloud server 192 to evaluate wearer utilization of various hearingdevice modes (e.g., Edge Mode, Mask Mode) and acoustic environmentclassifications and adaptations. With permission of the wearer, thereceived data can be subject to machine learning for purposes ofimproving the wearer's listening experience. Machine learning can beimplemented to capture data concerning the various acoustic environmentclassifications and adaptations, the wearer's switching pattern betweendifferent hearing device modes, and the wearer's overriding of thehearing device classifier. Using machine learning data produced by thecloud processor and transferred back to the hearing device 100 via thesmart charger 190 and/or communication device 136, the machine learningprocessor 160 of hearing device 100 can refine or optimize its acousticenvironment classification and adaptation mechanism. For example, basedon the wearer's activity, the machine learning processor 160 can beconfigured to enter Edge Mode automatically when a particular acousticenvironment is detected or prompt for engagement of Edge Mode (e.g., “doyou want to engage Edge Mode?”).

It is noted that FIGS. 1A, 1B, 1C, and 15 each describe an exemplaryear-worn electronic device 100 with various components. However, it willbe appreciated that each of the sensor arrangement 134, the sensor(s)150, the external electronic device 152, the rechargeable power source124, the charging circuitry 126, the machine learning processor 160, thesmart charger 190, and the cloud server 192 are optional/preferably.Therefore, it will be appreciated by the person skilled in the art thatthe ear-worn electronic device 100 may have any combination ofcomponents including processor 120, main memory 122, non-volatile memory123, classification module 138, microphone(s) 130, control input 129,communication device(s) 136, acoustic transducer 132, anduser-actuatable control 127.

It will be appreciated by the person skilled in the art that theear-worn electronic device 100 may have any combination of componentsincluding processor 120, main memory 122, non-volatile memory 123,classification module 138, microphone(s) 130, control input 129,communication device(s) 136, acoustic transducer 132, user-actuatablecontrol 127, sensor-actuatable control 128, and sensor(s) 150.

It will be appreciated by the person skilled in the art that theear-worn electronic device 100 may have any combination of componentsincluding processor 120, main memory 122, non-volatile memory 123,classification module 138, microphone(s) 130, control input 129,communication device(s) 136, acoustic transducer 132, user-actuatablecontrol 127, sensor-actuatable control 128, sensor(s) 150, and externalelectronic device 152.

It will be appreciated by the person skilled in the art that theear-worn electronic device 100 may have any combination of componentsincluding processor 120, main memory 122, non-volatile memory 123,classification module 138, microphone(s) 130, control input 129,communication device(s) 136, acoustic transducer 132, user-actuatablecontrol 127, and machine learning processor 160.

It will be appreciated by the person skilled in the art that theear-worn electronic device 100 may have any combination of componentsincluding processor 120, main memory 122, non-volatile memory 123,classification module 138, microphone(s) 130, control input 129,communication device(s) 136, acoustic transducer 132, user-actuatablecontrol 127, sensor-actuatable control 128, sensor(s) 150, and machinelearning processor 160.

It will be appreciated by the person skilled in the art that theear-worn electronic device 100 may have any combination of componentsincluding processor 120, main memory 122, non-volatile memory 123,classification module 138, microphone(s) 130, control input 129,communication device(s) 136, acoustic transducer 132, user-actuatablecontrol 127, sensor-actuatable control 128, sensor(s) 150, externalelectronic device 152, and machine learning processor 160.

It will be appreciated by the person skilled in the art that one or moreof the processor 120, the methods implemented using the processor 120,the machine learning processor 160, and the methods implemented usingthe machine learning processor 160 can be components of an externaldevice or system configured to communicatively couple to the hearingdevice 100, such as a smartphone or a smart watch. It will also beappreciated by the person skilled in the art that the microphone(s) 130can be one or more microphones of an external device or systemconfigured to communicatively couple to the hearing device 100, such asa smartphone or a smart watch.

All references and publications cited herein are expressly incorporatedherein by reference in their entirety into this disclosure, except tothe extent they may directly contradict this disclosure. Unlessotherwise indicated, all numbers expressing feature sizes, amounts, andphysical properties used in the specification and claims may beunderstood as being modified either by the term “exactly” or “about.”Accordingly, unless indicated to the contrary, the numerical parametersset forth in the foregoing specification and attached claims areapproximations that can vary depending upon the desired propertiessought to be obtained by those skilled in the art utilizing theteachings disclosed herein or, for example, within typical ranges ofexperimental error.

The recitation of numerical ranges by endpoints includes all numberssubsumed within that range (e.g. 1 to 5 includes 1, 1.5, 2, 2.75, 3,3.80, 4, and 5) and any range within that range. Herein, the terms “upto” or “no greater than” a number (e.g., up to 50) includes the number(e.g., 50), and the term “no less than” a number (e.g., no less than 5)includes the number (e.g., 5).

The terms “coupled” or “connected” refer to elements being attached toeach other either directly (in direct contact with each other) orindirectly (having one or more elements between and attaching the twoelements). Either term may be modified by “operatively” and “operably,”which may be used interchangeably, to describe that the coupling orconnection is configured to allow the components to interact to carryout at least some functionality (for example, a radio chip may beoperably coupled to an antenna element to provide a radio frequencyelectric signal for wireless communication).

Terms related to orientation, such as “top,” “bottom,” “side,” and“end,” are used to describe relative positions of components and are notmeant to limit the orientation of the embodiments contemplated. Forexample, an embodiment described as having a “top” and “bottom” alsoencompasses embodiments thereof rotated in various directions unless thecontent clearly dictates otherwise.

Reference to “one embodiment,” “an embodiment,” “certain embodiments,”or “some embodiments,” etc., means that a particular feature,configuration, composition, or characteristic described in connectionwith the embodiment is included in at least one embodiment of thedisclosure. Thus, the appearances of such phrases in various placesthroughout are not necessarily referring to the same embodiment of thedisclosure. Furthermore, the particular features, configurations,compositions, or characteristics may be combined in any suitable mannerin one or more embodiments.

The words “preferred” and “preferably” refer to embodiments of thedisclosure that may afford certain benefits, under certaincircumstances. However, other embodiments may also be preferred, underthe same or other circumstances. Furthermore, the recitation of one ormore preferred embodiments does not imply that other embodiments are notuseful and is not intended to exclude other embodiments from the scopeof the disclosure.

As used in this specification and the appended claims, the singularforms “a,” “an,” and “the” encompass embodiments having pluralreferents, unless the content clearly dictates otherwise. As used inthis specification and the appended claims, the term “or” is generallyemployed in its sense including “and/or” unless the content clearlydictates otherwise.

As used herein, “have,” “having,” “include,” “including,” “comprise,”“comprising” or the like are used in their open-ended sense, andgenerally mean “including, but not limited to.” It will be understoodthat “consisting essentially of” “consisting of,” and the like aresubsumed in “comprising,” and the like. The term “and/or” means one orall of the listed elements or a combination of at least two of thelisted elements.

The phrases “at least one of,” “comprises at least one of,” and “one ormore of” followed by a list refers to any one of the items in the listand any combination of two or more items in the list.

1. An ear-worn electronic device configured to be worn in, on or aboutan ear of a wearer, comprising: at least one microphone configured tosense sound in an acoustic environment; an acoustic transducer; anon-volatile memory configured to store a plurality of parameter valuesets, each of the parameter value sets associated with a differentacoustic environment; a control input configured to receive a controlinput signal produced by at least one of a user-actuatable control ofthe ear-worn electronic device and an external electronic devicecommunicatively coupled to the ear-worn electronic device in response toa user action; and a processor operably coupled to the microphone, theacoustic transducer, the non-volatile memory, and the control input, theprocessor configured to classify the acoustic environment using thesensed sound and apply, in response to the control input signal, one ofthe parameter value sets appropriate for the classification.
 2. Thedevice according to claim 1, wherein: the user-actuatable controlcomprises one or more of a button disposed on the device, a sensorresponsive to a touch or a tap by the wearer, a voice recognitioncontrol implemented by the processor, and gesture detection circuitryresponsive to a wearer gesture made in proximity to the device; and theexternal electronic device communicatively coupled to the ear-wornelectronic device comprises one or more of a personal digital assistant,a smartphone, a smart watch, a tablet, and a laptop.
 3. The deviceaccording to claim 1, wherein each of the parameter value sets comprisesa set of gain values or gain offsets associated with a differentacoustic environment, and one or both of: a set of noise-reductionparameters associated with the different acoustic environments; and aset of microphone mode parameters associated with the different acousticenvironments.
 4. The device according to claim 1, wherein the parametervalue sets comprise: a normal parameter value set associated with anormal or default acoustic environment; a plurality of other parametervalue sets each associated with a different acoustic environment; andeach of the other parameter value sets defines offsets to parameters ofthe normal parameter value set.
 5. The device according to claim 1,comprising: a sensor arrangement comprising one or more sensorsconfigured to sense, and produce sensor signals indicative of, one ormore of a physical state, a physiologic state, and an activity status ofthe wearer; and the processor is configured to receive the sensorsignals, classify the acoustic environment using the sensed sound, andapply, in response to the control input, one of the parameter value setsappropriate for the classification and one or more of the physicalstate, the physiologic state, and the activity status of the wearer. 6.The device according to claim 5, wherein the one or more sensorscomprise one or both of a motion sensor and a physiologic sensor.
 7. Thedevice according to claim 1, wherein the processor is configured toapply one of the parameter value sets that enhance intelligibility ofspeech in the acoustic environment.
 8. The device according to claim 1,wherein the acoustic environment includes muffled speech, and theprocessor is configured to: classify the acoustic environment as anacoustic environment including muffled speech using the sensed sound;and apply a parameter value set that enhances intelligibility of muffledspeech.
 9. The device according to claim 1, wherein, subsequent toapplying an initial parameter value set appropriate for an initialclassification of a current acoustic environment in response toreceiving an initial control input signal, the processor is configuredto: automatically apply an adapted parameter value set appropriate forthe initial or a subsequent classification of the current acousticenvironment in the absence of receiving a subsequent control inputsignal by the processor.
 10. The device according to claim 1, whereinthe processor is configured to: apply one or more different parametervalue sets appropriate for the classification of a current acousticenvironment in response to one or more subsequently received controlinput signals; learn wearer preferences using utilization data acquiredduring application of the different parameter value sets by theprocessor; and adapt selection of subsequent parameter value sets by theprocessor for subsequent use in the current acoustic environment usingthe learned wearer preferences.
 11. The device according to claim 1,wherein the processor is configured to: apply one or more differentparameter value sets appropriate for the classification of a currentacoustic environment in response to one or more subsequently receivedcontrol input signals; store, in the memory, one or both of utilizationdata and contextual data acquired by the processor during application ofthe different parameter value sets associated with the current acousticenvironment; and adapt selection of subsequent parameter value sets bythe processor for subsequent use in the current acoustic environmentusing one or both of the utilization data and the contextual data. 12.The device according to claim 1, wherein the processor is configuredwith instructions to implement a machine learning algorithm to:automatically apply an adapted parameter value set appropriate for aninitial or a subsequent classification of a current acousticenvironment.
 13. A method implemented by an ear-worn electronic deviceconfigured to be worn in, on or about an ear of a wearer, the methodcomprising: storing a plurality of parameter value sets in non-volatilememory of the device, each of the parameter value sets associated with adifferent acoustic environment; sensing sound in an acousticenvironment; classifying, by a processor of the device, the acousticenvironment using the sensed sound; receiving, by the processor, acontrol input signal produced by at least one of a user-actuatablecontrol of the device and an external electronic device communicativelycoupled to the device in response to a user action; and applying, by theprocessor in response to the control input signal, one of the parametervalue sets appropriate for the classification.
 14. The method accordingto claim 13, comprising: sensing, using a sensor arrangement of thedevice, one or more of a physical state, a physiologic state, and anactivity status of the wearer; producing, by the sensor arrangement,sensor signals indicative of one or more of the physical state, thephysiologic state, and the activity status of the wearer; and applying,by the processor in response to the control input signal, one of theparameter value sets appropriate for the classification and one or moreof the physical state, the physiologic state, and the activity status ofthe wearer.
 15. The method according to claim 13, wherein the processoris configured to apply one of the parameter value sets that enhanceintelligibility of speech in the acoustic environment.
 16. The deviceaccording to claim 1, wherein the processor is configured withinstructions to implement a machine learning algorithm to learn wearerpreferences using utilization data acquired during application ofdifferent parameter value sets applied by the processor.
 17. The deviceaccording to claim 16, wherein the processor is configured withinstructions to implement a machine learning algorithm to adaptselection of subsequent parameter value sets by the processor forsubsequent use in a current acoustic environment using learned wearerpreferences.
 18. The device according to claim 16, wherein the processoris configured with instructions to implement a machine learningalgorithm to adapt selection of subsequent parameter value sets forsubsequent use in a current acoustic environment using the utilizationdata and contextual data.
 19. The method according to claim 13, whereinthe acoustic environment includes muffled speech, and the methodcomprises: classifying the acoustic environment as an acousticenvironment including muffled speech using the sensed sound; andapplying a parameter value set that enhances intelligibility of muffledspeech.
 20. The method according to claim 13, wherein, subsequent toapplying an initial parameter value set appropriate for an initialclassification of a current acoustic environment in response toreceiving an initial control input signal, the method comprises:automatically applying an adapted parameter value set appropriate forthe initial or a subsequent classification of the current acousticenvironment in the absence of receiving a subsequent control inputsignal by the processor.
 21. The method according to claim 13,comprising: applying one or more different parameter value setsappropriate for the classification of a current acoustic environment inresponse to one or more subsequently received control input signals;learning wearer preferences using utilization data acquired duringapplication of the different parameter value sets by the processor; andadapting selection of subsequent parameter value sets by the processorfor subsequent use in the current acoustic environment using the learnedwearer preferences.
 22. The method according to claims 13, comprising:applying one or more different parameter value sets appropriate for theclassification of a current acoustic environment in response to one ormore subsequently received control input signals; storing, in thememory, one or both of utilization data and contextual data acquired bythe processor during application of the different parameter value setsassociated with the current acoustic environment; and adapting selectionof subsequent parameter value sets by the processor for subsequent usein the current acoustic environment using one or both of the utilizationdata and the contextual data.